JP6611776B2 - Image processing apparatus, method of operating image processing apparatus, and program - Google Patents

Description

The present invention relates to an image processing apparatus, an operation method of the image processing apparatus, and a program.

  Eye examinations are being conducted for the purpose of early diagnosis of lifestyle-related diseases and diseases that occupy the top causes of blindness. Ophthalmic tomographic imaging devices such as optical coherence tomography (OCT) are expected to be useful for more accurate diagnosis because they can observe the internal state of the retinal layer in three dimensions. . In order to quantitatively measure the thickness of the layer, the boundary of each layer of the retina is detected from the tomogram using a computer. For example, as shown in FIG. 10B, an inner boundary membrane B1, an inner reticular layer boundary B2, a photoreceptor inner / outer segment boundary B3 and a retinal pigment epithelium layer boundary B4 are detected, and the retinal thickness T1 or GCC (Ganglion Cell Complex) ) Measure the thickness T2.

  The tomographic image of the eye taken by OCT contains regions (cells, tissues, and parts) that affect visual function, and doctors can select cells that are relevant when estimating the possibility of recovery or prognosis of visual function. , The thickness of the layer, the thickness of the layer, and the positional relationship with the site such as the fovea F1 (FIG. 10A) are observed. For example, when the area of Ro in FIG. 10A is enlarged and displayed, it is represented as shown in FIG. The light reaching the retina is converted into an electrical signal in the outer segment L1 of the photoreceptor cell C1, and is perceived in the visual cortex (not shown) of the cerebrum via the bipolar cell C2 via the ganglion cell C3 and the optic nerve (not shown). Is done. If the photoreceptor outer segment L1 is lost, it becomes impossible to convert light into an electrical signal, so that the visual function deteriorates at the defective site. The photoreceptor cell C1 has a cone and a rod, and the cone controls the visual function in a light place. As shown in FIG. 10 (c), the cones are present at higher density as they are closer to the fovea F1, so that the influence on the visual function per unit area of the visual cell outer segment defect is greater as it is closer to the fovea F1. The visual axis is a line connecting the object to be watched and the fovea F1. Therefore, in order to estimate the effect of visual cell outer segment defects on visual function, the degree of defect (length of outer segment) and the existence area (area ), It is necessary to consider the distance from the fovea of the defect area.

  Further, since the photoreceptor cell C1 receives nutrition from the choroidal blood vessel V1 as shown in FIG. 10 (d), if there is a lesion such as retinal detachment RD between the photoreceptor outer segment L1 and the retinal pigment epithelium L2, the photoreceptor cell C1 is nourished. Can not supply. Therefore, the presence of the retinal detachment RD increases the possibility that the visual function will deteriorate in the future. Further, if the presence of the retinal detachment RD is prolonged, the photoreceptor cell C1 is killed and the visual function cannot be recovered. Therefore, in order to estimate the effect on the visual function from the retina shape and predict the prognosis of the visual function, not only simply measuring the thickness of the cells and layers but also the positional relationship with the site such as the fovea F1, It is necessary to consider the presence or absence of a lesion such as retinal detachment RD under the fovea F1. To date, for the purpose of supporting diagnosis of glaucoma and optic nerve disease, Patent Document 1 discloses a technique for measuring a GCC thickness T2 associated with a visual field, which is one of visual functions, and displaying a difference from the GCC thickness T2 in a normal eye as a map. Is disclosed.

WO / 2008/157406 JP 2009-34480 A

  However, the technique described in Patent Document 1 assumes application to glaucoma, and does not consider the positional relationship with the fovea F1 to be considered in the case of macular disease and the effect on visual function due to exudative lesions. . In addition, the technique described in Patent Document 1 only presents (in parallel) one or more layer thickness maps examined so far for visual function recovery possibility and prognosis prediction, and visual function recovery is possible. The sex and prognosis had to be judged visually from the map.

  In addition, the technique described in Patent Document 2 is based on the premise that there is an inspection result by a perimeter, and the correspondence with the visual function evaluation value by the visual field inspection by measuring the layer thickness of the part where abnormality is recognized by the visual field inspection. Check out. It is known that a change in retinal shape occurs prior to a change in visual function, and it is desirable from the viewpoint of early detection of retinal diseases that prognosis can be predicted based only on information obtained from ocular tomograms.

  The present invention provides an image processing technique capable of providing information relating to visual functions based on information on a sieve plate.

An image processing apparatus according to one aspect of the present invention that achieves the above object includes an acquisition unit that acquires a tomographic image obtained by imaging the fundus of a subject's eye by Fourier-domain OCT ;
A layer boundary is detected from the tomographic image acquired by the acquisition unit, and an area having a width in the depth direction of the fundus is used as a slab-shaped plate region automatically using the detected layer boundary. And detecting means for detecting.

  According to the configuration of the present invention, it is possible to provide an image processing technique capable of providing information relating to the visual function based on information on the sieve plate.

1 is a diagram illustrating a functional configuration example of an image processing apparatus according to a first embodiment. FIG. FIG. 3 is a diagram illustrating a flow of processing executed by the image processing apparatus according to the first embodiment. The figure explaining the image processing content in 1st Embodiment. The figure which shows the detail of the process performed by S240 which concerns on 1st Embodiment. The figure which shows the detail of the process performed by S250 which concerns on 1st Embodiment. The figure explaining the image processing content in 2nd Embodiment. The figure explaining the image processing content in 3rd Embodiment. The figure which shows the detail of the process performed by S240 which concerns on 3rd Embodiment. The figure which shows the detail of the process performed by S250 which concerns on 3rd Embodiment. The schematic diagram regarding the tomogram of the macular part of the retina image | photographed by OCT. 1 is a diagram illustrating a configuration example of a system including an image processing apparatus according to an embodiment. The figure which shows the hardware structural example of an image processing apparatus.

(First embodiment)
The image processing apparatus according to the present embodiment is configured to predict the influence on the visual function and the prognosis based on the positional relationship between the thinned region of the photoreceptor outer segment thickness and the fovea F1, the presence or absence of retinal detachment RD, and the like. It is a thing. Hereinafter, an image processing apparatus and an image processing method in the image processing apparatus according to embodiments of the present invention will be described in detail with reference to the accompanying drawings.

  FIG. 12 is a diagram illustrating a hardware configuration of the image processing apparatus 10. The CPU 1201 controls the overall operation of the image processing apparatus 10. The ROM 1203 and the external storage device 1204 store programs and parameters that can be executed by the image processing apparatus 10. The RAM 1202 functions as a work area used when the program is executed. The monitor 1205 functions as an output unit, and the monitor 1205 displays the result of image processing and the result of calculation processing. A keyboard 1206 and a mouse 1207 function as an input unit, and a user can input various commands to the image processing apparatus via the keyboard 1206 and the mouse 1207. The interface 1208 connects the image processing apparatus 10 to the network (LAN) 30. Each component shown in FIG. 12 is connected via a bus 1209.

  FIG. 11 is a configuration diagram of devices connectable to the image processing apparatus 10. The tomographic imaging apparatus 20 can be connected to the data server 40 via a network (LAN) 30 such as Ethernet (registered trademark). The image processing apparatus 10 can be connected to the tomographic imaging apparatus 20 via a network (LAN) 30 or an interface such as an optical fiber, USB, or IEEE1394. The tomographic image capturing apparatus 20 is an apparatus that captures a tomographic image of the eye, and is, for example, a time domain type or Fourier domain type OCT. The tomographic image capturing apparatus 20 three-dimensionally captures a tomographic image of an eye not shown (not shown) in response to an operation by an operator (not shown). The captured tomographic image is transmitted to the image processing apparatus 10. The data server 40 is a server that holds a tomographic image of the eye to be examined, an image feature amount of the eye to be examined, and stores the tomographic image of the eye to be examined output by the tomographic imaging apparatus 20 and the analysis result output by the image processing apparatus 10. . In response to a request from the image processing apparatus 10, the data server 40 transmits past data related to the eye to be examined to the image processing apparatus 10.

  In the present embodiment, the case where the image processing apparatus 10 measures the inner cell outer segment boundary B3 and the retinal pigment epithelium layer boundary B4 will be described. However, the present invention is not necessarily limited thereto. It is possible to detect other layer boundaries (outer boundary membrane (not shown), inner boundary of retinal pigment epithelium layer (not shown), etc.).

  FIG. 1 is a block diagram illustrating a functional configuration of the image processing apparatus 10. The image processing apparatus 10 includes a tomographic image acquisition unit 110, a storage unit 120, an image processing unit 130, a diagnosis support information output unit 140, and an instruction acquisition unit 150. Have. The image processing unit 130 includes an eye feature acquisition unit 131, a lesion detection unit 132, and a visual function influence degree determination unit 133. The visual function influence degree determination unit 133 includes a visual function recovery possibility determination unit 1331 and a visual function prognosis prediction unit 1332. The diagnosis support information output unit 140 includes a visual function influence degree information output unit 141, a visual function recoverability information output unit 142, and a visual function prognosis prediction information output unit 143.

  The function of each block constituting the image processing apparatus 10 will be described in association with a specific execution procedure of the image processing method in the image processing apparatus 10 shown in the flowchart of FIG. In step S210, the tomographic image acquisition unit 110 transmits a tomographic image acquisition request to the tomographic imaging apparatus 20. The tomographic imaging apparatus 20 transmits a corresponding tomographic image in response to the acquisition request. The tomogram acquisition unit 110 receives a tomogram corresponding to the acquisition request from the tomogram imaging apparatus 20 via the network 30. The tomographic image acquisition unit 110 stores the received tomographic image in the storage unit 120.

  In step S220, the eye feature acquisition unit 131 acquires information on a predetermined part and position information on a predetermined tissue structure from the tomographic image. The eye feature acquisition unit 131 acquires eye features (anatomical features) representing predetermined cells, tissues, or parts from a tomographic image. The eye feature acquisition unit 131 detects, for example, the inner boundary film B1, the photoreceptor inner / outer segment boundary B3, and the retinal pigment epithelium layer boundary B4 as the eye features from the tomograms stored in the storage unit 120. Is possible. Then, the eye feature acquisition unit 131 stores each detected feature in the storage unit 120.

  A specific procedure for acquiring eye features will be described. First, a processing method for performing layer boundary detection will be described. Here, the volume image to be processed is considered as a set of two-dimensional tomographic images (B-scan images), and the following processing is performed on each two-dimensional tomographic image. First, smoothing processing is performed on the focused two-dimensional tomographic image to remove noise components. Next, edge components are detected from the two-dimensional tomographic image, and some line segments are extracted as layer boundary candidates based on their connectivity. Then, the top line segment is selected as the inner boundary film B1 from the extracted layer boundary candidates. Further, the line segment having the maximum contrast located on the outer layer side (the side having the larger z coordinate in FIG. 10A) than the inner boundary film B1 is selected as the photoreceptor inner / outer segment boundary B3. Further, the lowermost line segment from the extracted candidates is selected as the retinal pigment epithelium layer boundary B4.

  Furthermore, a precise shape extraction may be performed by applying a variable shape model such as Snakes or level set method using these line segments as initial values. Further, the layer boundary may be detected by a graph cut method. Note that boundary detection using a deformable model or graph cut may be performed three-dimensionally on the volume image, or two-dimensionally applied to each two-dimensional tomographic image. As a method for detecting the layer boundary, any method may be used as long as the layer boundary can be detected from the tomographic image of the eye.

  In step S230, the lesion detection unit 132 determines a lesion candidate (in a region sandwiched between the intraocular segment outer segment boundary B3 and the retinal pigment epithelium layer boundary B4 determined by the characteristics of the eye acquired in S220. The presence or absence of retinal detachment is detected. In the present embodiment, a case where the retinal detachment RD exists under the fovea will be described. Retinal detachment RD may occur in any part of the fundus other than Marriott blind spot, and even if retinal detachment RD occurs in any part within the tomographic imaging range (excluding Marriott blind spot) The image processing method in the form is applicable.

  A method for detecting retinal detachment RD will be described. A lesion candidate region is detected by comparing the luminance in the region determined by the eye features (anatomical features) with a predetermined luminance threshold Trd. For example, a region having a low luminance value with respect to the threshold is detected as a lesion candidate. As shown in FIGS. 3 (a) and 3 (c), on each A scan line in the x-axis direction on the xz plane, the outer layer side (the side with the larger z-coordinate) and the retina than the inner cell outer segment boundary B3. A low luminance region in the region on the inner layer side (side where the z coordinate is small) from the pigment epithelium layer boundary B4 is extracted as a lesion candidate region. A known extraction method can be applied as the extraction method, and here, a region having a luminance lower than the luminance threshold Trd in the region described above is detected as a retinal detachment candidate region (lesion candidate region).

  The lesion detection unit 132 performs visual inspection from the positions of the eye features (fovea F1, inner boundary membrane B1, photoreceptor inner / outer segment boundary B3, retinal pigment epithelium layer boundary B4) and lesion candidate (retinal detachment RD). Quantify the size of the part responsible for the function. The lesion detection unit 132 measures (quantifies) the thickness of the photoreceptor outer segment L1 (visual cell outer segment thickness) Tp (x, y) at each coordinate point in the xy plane as the relationship between the position and the size. To do. The photoreceptor outer segment L1 that controls the visual function is a part that controls the visual function by converting light reaching the retina of the eye of the subject into an electrical signal. Here, as a simple method of measuring Tp (x, y), the z axis between the inner and outer segment boundary B3 of the photoreceptor cell and the retinal pigment epithelium layer boundary B4 on each A scan line in the x axis direction on the xz plane. A value obtained by subtracting the distance in the z-axis direction of the retinal detachment region from the distance in the direction is used. Note that the method of measuring the thickness of the photoreceptor outer segment L1 is not limited to this. In step S220, the inner boundary (not shown) of the retinal pigment epithelial layer is extracted, and the retinal detachment thickness is calculated from the distance between the photoreceptor inner / outer segment boundary B3 and the inner boundary (not shown) of the retinal pigment epithelium layer. The subtracted value may be Tp (x, y).

Next, the lesion detection unit 132 receives normal value data about the normal value Tn of the photoreceptor outer segment thickness from the data server 40 and the density distribution ω (a) [pieces / mm ² ] of photoreceptor cells in the xy plane (FIG. 3 (b)). Here, data on cones among the photoreceptor cells is acquired. Further, the horizontal axis a is the viewing angle [°] from the visual axis (line connecting the crystalline lens and the fovea).

  In step S240, the visual function influence determination unit 133 calculates an evaluation value from the relationship between the information on the predetermined part and the position of the predetermined tissue structure. The visual function influence determination unit 133 calculates the influence degree on the visual function using the thickness (visual cell outer segment thickness) Tp (x, y) of the photoreceptor outer segment L1 quantified with respect to the site that controls the vision function. To do. In this step, it is also possible to determine the possibility of recovery of visual function and the possibility of deterioration of visual function (prognosis prediction). Details of this step will be described later with reference to FIG.

  In step S250, the diagnosis support information output unit 140 outputs the result determined by the visual function influence degree determination unit 133 as diagnosis support information. A two-dimensional coordinate map (visual function influence degree map) having the visual function influence value calculated in the determination in S240 as the pixel value of the tomographic image is generated and output as diagnosis support information. Further, the diagnosis support information output unit 140 generates a two-dimensional coordinate map (visual function recovery possibility map) having an index (value) indicating the possibility of recovery of the visual function as a pixel value of the tomographic image, and performs diagnosis support It can also be output as information. In addition, the diagnosis support information output unit 140 generates a two-dimensional coordinate map (visual function deterioration risk map) having an index (value) indicating a possibility that the visual function is deteriorated as a pixel value of the tomographic image, and performs diagnosis support. It can also be output as information. Here, the visual function influence degree map, the visual function recoverability map, and the visual function lowering risk degree map are included in the diagnosis support information, and the results determined by the visual function influence degree determination unit 133 are expressed as two-dimensional coordinates. The determination result is visualized by display control shown on the map.

  In step S260, the instruction acquisition unit 150 acquires an instruction from the outside as to whether or not to save the current processing result relating to the eye to be examined in the data server 40. This instruction is input by the operator via, for example, the keyboard 1206 and the mouse 1207 shown in FIG. If storage of the processing result is instructed, the process proceeds to S270, and if storage is not instructed, the process proceeds to S280.

  In step S270, the diagnosis support information output unit 140 associates the examination date and time, the identification information for identifying the eye to be examined, the tomogram, the analysis result of the image processing unit 130, and the diagnosis support information obtained by the diagnosis support information output unit 140 in association with the data. Send to server 40.

  In step S280, the instruction acquisition unit 150 acquires an instruction from the outside as to whether or not to end the tomographic analysis processing by the image processing apparatus 10. This instruction is input by the operator via the keyboard 1206 and the mouse 1207 shown in FIG. If an instruction to end the process is acquired, the analysis process ends. On the other hand, if an instruction to continue the process is acquired, the process returns to S210, and the process for the next eye to be examined (or reprocessing for the same eye to be examined) is performed.

(Visual function influence determination processing)
Next, details of the process executed in S240 will be described with reference to FIG. In S410, the ratio (Tn / Tp) of the normal value data (Tn) related to the size of the part to the size Tp (x, y) of the part governing the visual function quantified by the lesion detection unit 132 is obtained. Then, this ratio is multiplied by normal value data (ω (a)) indicating the density distribution of photoreceptor cells to calculate a value indicating the degree of influence. The visual function influence determination unit 133 can acquire, from the data server 40, for example, normal value data (Tn) relating to the size of a part that controls the visual function and data (ω (a)) indicating the density distribution of the photoreceptor cells. Is possible. Specifically, the degree of influence S ₁ on the visual function is calculated by the following equation (1). For the calculation of the value indicating the degree of influence on the visual function, the photoreceptor outer segment thickness Tp (x, y) at each point on the xy plane detected in S230 and the position and shape of the photoreceptor outer segment (for example, , (Thickness) normal value Tn, normal value data relating to the density distribution ω (a) of photoreceptor cells in the xy plane is used. Here, the case of visual acuity is considered as the visual function.

S ₁ = Σ {ω (a) · Tn / Tp (x, y)} (1)
Here, Tp (x, y) / Tn represents the degree of thinning of the outer cone of the cone, and in this embodiment, Tn = 60 μm. From equation (1), it can be seen that the wider the thinned area of the cone outer segment and the closer the thinned area is to the fovea, the greater the influence on visual function.

In step S420, the visual function recovery possibility determination unit 1331 uses the ratio between the outer thickness of the photoreceptor cell and the normal value of the outer cell thickness obtained in S230, as in the following equation (2), xy The visual function recovery possibility R ₁ is determined at each point (x, y) on the plane. Here, RD (t) is the existence period of retinal detachment.

If the ratio Tp / Tn is less than the threshold regarding the ratio (<Tr), the visual function recovery possibility determination unit 1331 determines that the reproduction of the photoreceptor outer segment is difficult, and the visual function at the position (x, y) is determined. The index (value) indicating the possibility of recovery is calculated as zero. If Tp / Tn is equal to or greater than the threshold regarding the ratio (≧ Tr), the visual function recovery possibility is calculated by the equation (2 ′). In addition, the longer the lesion candidate existence period (retinal detachment RD existence period RD (t)), the worse the photoreceptor nutritional state, so multiply by (1 / lesion candidate existence period (RD (t)). by, R ₁ is composed of a lower value. in the case where information existing period retinal detachment RD (t) is not obtained from the data server 40 as RD (t) = 1 in (2 ') wherein R ₁ is calculated.

In step S430, the visual function prognosis unit 1332 calculates hypofunction risk P ₁ viewed as an index (value) regarding prognosis of visual function. This is an index (value) indicating the possibility that the visual function will deteriorate in the future. Here, RDe (x, y) is a value set according to the presence or absence of a lesion candidate (retinal detachment) at each point (x, y) on the xy plane. For example, the lesion detection unit 132 sets RDe (x, y) = 1 if there is retinal detachment at position coordinates (x, y), and RDe (x, y) = 0 if there is no retinal detachment. The visual function prognosis predicting unit 1332 acquires normal value data (ω) indicating the density distribution of photoreceptor cells and setting data (RDe) set in accordance with the presence or absence of a lesion candidate. Perform the operation.

P ₁ = Σ {ω (a) · RDe (x, y)}} (3)
If the retinal detachment existence period RD (t) at each point (x, y) on the xy plane is known, an estimated value P ₁ ′ of the visual function deterioration time is generated by (3 ′). It is also possible.

P ₁ '= Σ {ω (a) · (Td−RD (t))} (3 ′)
Here, Td is data representing the maximum period during which the photoreceptor outer segment can survive without nutrient supply from the choroid, and can be acquired from the data server 40 or the storage unit 120.

(Diagnosis support information output process)
Next, details of the process executed in S250 will be described with reference to FIG.

  In step S <b> 510, the diagnosis support information output unit 140 acquires data on the visual function influence degree, the visual function recovery possibility, and the visual function lowering risk degree from the image processing unit 130. Then, the diagnosis support information output unit 140 transmits the respective information to the visual function influence degree information output unit 141, the visual function recoverability information output unit 142, and the visual function prognosis prediction information output unit 143.

In step S520, the visual function influence degree information output unit 141 calculates the influence degree on the visual function at each point on the xy plane calculated in step S410, that is, ω (a) · Tn / Tp (x, A map having y) as a value is generated. This map visualizes the degree of influence on the visual function, and represents the degree of visual function influence at each point (x, y) at the time of shooting. Further, on the monitor 1205 (FIG. 12), not only the visual function influence map but also the visual function influence S ₁ calculated in step S410 is simultaneously output.

  In step S530, the visual function recovery possibility information output unit 142 calculates the visual function recovery possibility value ((Tp (x, y) / Tn) ·) at each point on the xy plane calculated in step S420. A map having (1 / RD (t)) or 0) is generated. Further, the visual function prognosis prediction information output unit 143 generates a map having the visual function deterioration risk value (ω (a) · RDe (x, y)) calculated in step S420.

  Here, when the existence period RD (t) of retinal detachment at each point (x, y) on the xy plane is known, the estimated value of the visual function deterioration time at each point (x, y), That is, a map having ω (a) · (Td−RD (t)) as a value is generated. Here, Td represents the maximum period in which the photoreceptor outer segment can survive without nutrient supply from the choroid.

In addition to the above map, the monitor 1205 can also output an index R ₁ relating to the visual function recovery possibility, an index P ₁ relating to the visual function lowering risk level, and an index P ₁ ′ relating to the estimated visual function lowering timing value.

  According to this embodiment, the image processing apparatus calculates a value indicating the degree of influence on the visual function according to the positional relationship between the thinned region of the photoreceptor outer segment thickness and the fovea. In addition, the presence or absence of retinal detachment is examined, and the visual function reduction risk is also calculated using information on the outer thickness of the photoreceptor cell and the presence or absence of retinal detachment. Thereby, the influence of the lesion candidate on the visual function and the prognosis of the visual function can be predicted from the tomographic image of the macular disease.

(Second Embodiment)
This embodiment considers a case where exudation lesions such as vitiligo EX accumulate under the fovea F1 rather than a layer shape abnormality as an effect on the visual function as compared with the first embodiment. The vitiligo EX is extracted, and the influence on the visual function and the prognosis of the visual function are predicted based on the positional relationship between the distribution of the vitiligo EX and the eye features such as the fovea F1.

The configuration of devices connectable to the image processing apparatus 10 according to the present embodiment and the functional configuration of the image processing apparatus 10 are the same as those in the first embodiment. The image processing flow in the present embodiment is the same as the image processing flow in the first embodiment (FIG. 2) except for the processing in S230, S240, and S250 in FIG. Therefore, only the processes in S230, S240, and S250 will be described here.
As shown in FIG. 6, in the retina of diabetic retinopathy, lipids and proteins leaked from the retinal blood vessels accumulate to form a massive high-luminance region called vitiligo EX. Vitiligo EX is often formed mainly in the vicinity of the outer reticulate layer, and the incident light is blocked at the site where the vitiligo EX exists, so that the light does not reach the photoreceptor cell C1 and the visual acuity is lowered. In particular, the closer the position of the vitiligo EX is to the fovea F1, the higher the cone density, and thus the greater the influence on the visual function.

  Further, it is empirically known that when the retinal detachment RD exists below the fovea F1, the white spots EX tend to accumulate toward the retinal detachment RD. Once the vitiligo EX accumulates under the fovea F1, the visual acuity is remarkably lowered and the recovery of visual acuity cannot be expected. Therefore, knowing the distance (closeness) of the vitiligo EX to the subretinal area and whether it is moving toward the subretinal area is useful in preventing deterioration of visual function due to accumulation of lesions under the fovea. It is thought that.

(Process of step S230)
In the process of step S230 in the second embodiment, the lesion detection unit 132 detects the white spot EX as a lesion candidate in the tomogram of the eye. Here, the luminance value information is combined with the output value of a filter that emphasizes a block structure such as a point concentration filter, and is identified as follows. That is, a region where the output of the point concentration degree filter is equal to or higher than the threshold value Ta and the luminance value on the tomographic image is equal to or higher than the threshold value Tb is defined as a white spot EX. The detection method of vitiligo EX is not limited to this, and any known lesion detection method can be used. Similarly to the case of the first embodiment, the presence / absence of retinal detachment RD is detected in the region sandwiched between the photoreceptor inner / outer segment boundary B3 and the retinal pigment epithelium layer boundary B4 detected in S220.

  Next, the lesion detection unit 132 quantifies the distribution of the vitiligo EX in the retina from the detected lesions (the vitiligo EX and the retinal detachment RD). Specifically, the detected white spot EX is labeled. By labeling, a label value is set for each of the vitiligo EX. Here, the region expansion method is used as a labeling method. The labeling technique is not limited to this, and any known labeling technique can be used. Next, the volume of vitiligo EX having the same label value is obtained. Further, when the retinal detachment RD exists in the retina, the shortest distance dn from the white spot EX of the label n to the retinal detachment RD is obtained.

(Process of step S240)
Next, details of the processing executed in S240 in the second embodiment will be described with reference to the flowcharts shown in FIGS.

In step S410, the visual function influence degree determination unit 133 calculates a value S ₂ indicating a degree of influence on the visual function using the following equation (4). Here, the case of visual acuity is considered as the visual function.

S ₂ = ΣnΣarea {ω (a)} (4)
Here, ω (a) represents the same cone density [number / mm ² ] as in the first embodiment, area represents the area when the white spot EX having the same label is projected on the xy plane, and n represents imaging. It represents the number of vitiligo EX present in the region.

In step S420, the visual function recovery possibility determination unit 1331 calculates an index R ₂ regarding visual function recovery possibility using the following equation (5).

R ₂ = Σn {ω (a) · d _n } (5)
Where n is the total number of the detected white spots 16, v _n is the volume of vitiligo 16 of the label n, is d _n represents the distance between the center of gravity and retinal detachment 15 white spots 16 of the label n.

In step S430, the visual function prognosis prediction unit 1332 calculates the visual function decrease risk, that is, the lesion accumulation risk P ₂ under the fovea F1 using the following equation (6).
P ₂ = Σn {v _n / (d _n +1)} (6)
(Processing of step S250)
Next, details of the process executed in S250 will be described with reference to FIG.
Note that since S510 is the same as that in the first embodiment, a description thereof will be omitted.

In step S520, the visual function influence information output unit 141 has a map (value of visual function influence) indicating the degree of influence on the visual function at each point (x, y) on the xy plane calculated in S410. Degree map). Also, on the monitor 1205 in FIG. 12, not only the visual function influence degree map, the value S ₂ showing the visual function influence degree calculated in step S410 is also possible to simultaneously output.

In S530, the visual function recovery possibility information output unit 142 and the visual function prognosis prediction information output unit 143 include a map having a value indicating the visual function recovery possibility at each point on the xy plane calculated in S420, and A map having a value indicating the degree of visual function deterioration risk calculated in S430 is generated. Furthermore, on the monitor 1205 in FIG. 12, not only the visual function recovery possibility map and the visual function deterioration risk degree map, but also an index R ₂ and visual function deterioration risk degree P ₂ relating to the visual function recovery possibility can be output simultaneously. .

  With the configuration described above, the image processing apparatus 10 extracts the vitiligo EX and the retinal detachment RD as lesions, and predicts the effect on the visual function and the prognosis of the visual function based on the positional relationship with the eye features such as the fovea F1. can do.

(Third embodiment)
Compared to the first embodiment, the present embodiment considers a case where an influence on the visual function appears due to an abnormal shape of the tissue called a sieving plate L3 existing in the deep layer of the optic nerve head. Extraction and shape measurement of layer boundaries and phloem plate L3 in the retina, and effects on visual function and prognosis of visual function using not only the fovea F1 but also the positional relationship with eye features such as Marriott blind spot Is to be predicted.

  As shown in FIGS. 7 (a) and 7 (b), there is a sieve-like membrane called a sieve plate L3 under the optic disc. The sieve plate L3 has a disk shape when viewed from the xy plane and has approximately 600 to 700 holes. The optic nerve fiber bundle, that is, the axon aggregate of the ganglion cell C1, passes through the hole and extends toward the brain. When the intraocular pressure increases, the sieve plate L3 is bent and the position of the hole is shifted, and the axon is compressed, so that the ganglion cell C1 is killed.

  The configuration of devices connectable to the image processing apparatus 10 according to the present embodiment is the same as that of the first embodiment. Further, the functional configuration of the image processing apparatus 10 does not include the visual function recovery possibility determination unit 1331 in the visual function influence determination unit 133, and does not include the visual function recovery possibility information output unit 142 in the diagnosis support information output unit 140. This is different from the case of the first embodiment. This reflects that once the ganglion cell C1 is killed and the visual function is affected, it does not recover. The image processing flow in this embodiment is the same as the image processing flow in the first embodiment (FIG. 2) except for the processing in S220, S230, S240, and S250 in FIG. Therefore, only the processes in S220, S230, S240, and S250 will be described here.

(Process of step S220)
In the process of step S220 in the third embodiment, the eye feature obtaining unit 131 detects the optic disc and the fovea F1 by detecting a recess from the tomographic image. Further, retinal blood vessel extraction is performed in the projection image of the tomographic image, and the one where the retinal blood vessel exists in the recess is determined as the optic nerve head, and the one where it does not exist is determined as the fovea F1. Any known line enhancement filter is used as a method for extracting retinal blood vessels. Since the photoreceptor C1 does not exist in the depression of the optic nerve head, it cannot be perceived and is called the Marriott blind spot.

  Next, the inner boundary membrane B1, the nerve fiber layer boundary B5, and the retinal pigment epithelium layer boundary B4 are acquired as layer boundaries. Since the layer boundary acquisition method is the same as in the first embodiment, a description thereof is omitted here. Further, the sieve plate L3 is detected by the following procedure. In other words, at each point (x, y) in the region where the retinal pigment epithelium layer boundary B4 does not exist on the xy plane (Marriott blind spot), it is on the deep layer side (positive side of the z axis) from the inner boundary film B1. A high luminance region that is equal to or greater than the threshold Tx is detected. The sieve plate L3 is detected as a disk-shaped region having a large number of holes on the xy plane. In this embodiment, the nerve fiber layer boundary B5 is used, but the inner mesh layer boundary B2 may be obtained instead. In this case, however, GCC (Ganglion Cell Complex) thickness is measured instead of nerve fiber layer thickness in S230.

(Process of step S230)
In the process of step S230 in the third embodiment, the lesion detection unit 132 measures the nerve fiber layer thickness using the inner boundary membrane B1 and the nerve fiber layer boundary B5 acquired in step S220. Next, the normal value range of the nerve fiber layer thickness is acquired from the data server 40, and a point (x ′, y ′) outside the Marriott blind spot where the nerve fiber layer thickness is outside the normal value range is detected as a lesion. To do. Further, the lesion detection unit 132 measures the sieving plate thickness and the squeeze plate boundary unevenness, and points within the Marriott blind spot that are out of the normal value range of the sieving plate thickness and concavo-convex obtained from the data server 40 ( x, y) are detected as lesions.

(Process of step S240)
Next, details of the process executed in S240 will be described with reference to FIG. In step S810, the visual function influence degree determination unit 133 calculates a value S ₃ showing a functional effect of visual in accordance with equation (7) below from the nerve fiber layer thickness obtained in step S230.

Here, T _{n (x, y)} is the normal value (median value) of the nerve fiber layer thickness at the point (x, y) on the xy plane. T _{l (x, y)} is the nerve fiber layer thickness at the point (x, y) on the xy plane determined to be abnormal nerve fiber layer thickness in S230. α (x, y) is a variable that is 0 at the center Marriott blind spot and 1 otherwise.

In step S820, the visual function prognosis unit 1332, resulting thickness of the sieve-like plate L3 in step S230, the calculated hypofunction risk P ₃ viewed in accordance with the equation (8) follows from the irregularities.

P ₃ = Σx, y {T _ns / T _s } + k · ΣyΣx {π / θ _{x, y} } (8)
Here, θ _{x and y} are three adjacent control points constituting the boundary line of the sieve plate (three points x-1, x and x + 1 for the x-axis direction, y for the y-axis direction). This is the angle [rad] formed by the three points of -1, y, and y + 1. K is a proportionality constant.

(Processing of step S250)
Next, the details of the process executed in S250 will be described with reference to FIG.

  In step S910, the diagnosis support information output unit 140 acquires data on the visual function influence degree and the visual function lowering risk degree from the image processing unit 130, and the visual function influence degree information output unit 141 and the visual function prognosis prediction information output unit respectively. 143.

  In step S920, the visual function influence degree information output unit 141 has a map (value of visual function influence) indicating the degree of influence on the visual function at each point (x, y) on the xy plane calculated in step S810. Degree map). Further, on the monitor 1205 in FIG. 12, not only the visual function influence map but also the visual function influence index (value) calculated in S810 can be output at the same time.

  In step S930, the visual function prognosis prediction information output unit 143 generates a map having values indicating the visual function deterioration risk level at each point on the xy plane calculated in S820. Furthermore, on the monitor 1205 in FIG. 12, not only the visual function deterioration risk level map but also the visual function deterioration risk level calculated in S820 can be output at the same time.

  According to the above-described configuration, the image processing apparatus 10 extracts the abnormal shape (thinning or unevenness) of the phloem plate in the optic nerve head, and affects the visual function according to the positional relationship with the fovea and the Marriott blind spot. Calculate the degree and risk of visual function deterioration. Thereby, in an early glaucoma case, the influence on the visual function by the distribution of lesions under the retina can be determined from the ocular tomogram of the optic nerve head, and the prognosis of the visual function can be predicted.

(Other examples)
The present invention can also be realized by executing the following processing. That is, software (program) that realizes the functions of the above-described embodiments is supplied to a system or apparatus via a network or various storage media, and a computer (or CPU, MPU, or the like) of the system or apparatus reads the program. It is a process to be executed.

  DESCRIPTION OF SYMBOLS 10: Image processing apparatus, 110: Tomographic image acquisition part, 131: Eye part characteristic acquisition part, 132: Lesion detection part, 133: Visual function influence degree determination part, 140: Diagnosis support information output part

Claims (13)

Acquisition means for acquiring a tomographic image obtained by imaging the fundus of the eye to be examined by Fourier domain OCT;
A layer boundary is detected from the tomographic image acquired by the acquisition unit, and an area having a width in the depth direction of the fundus is used as a slab-shaped plate region automatically using the detected layer boundary. Detection means for detecting
An image processing apparatus comprising:   The image processing apparatus according to claim 1, wherein the layer boundary includes a retinal pigment epithelium layer boundary.   The image processing apparatus according to claim 1, further comprising a calculation unit that calculates risk information of the eye to be inspected based on the sieve plate region detected by the detection unit.   The image processing apparatus according to claim 3, further comprising display control means for displaying the risk information on a display means.   2. The measuring device according to claim 1, further comprising: a measuring unit configured to measure an analysis value related to a range of the sieve plate region in a depth direction of the fundus by analyzing the sieve plate region detected by the detection unit. 5. The image processing apparatus according to any one of items 4 to 4.   The image processing apparatus according to claim 5, wherein the measurement unit measures the thickness of the sieve plate region detected by the detection unit as the analysis value. An acquisition step in which the acquisition means acquires a tomographic image obtained by imaging the fundus of the eye to be examined by Fourier domain OCT;
The detection means detects a layer boundary from the tomographic image acquired in the acquisition step, and uses the detected layer boundary as a region having a width in the depth direction of the fundus as a phloem plate region. A detection process that automatically detects from the image;
A method of operating an image processing apparatus, comprising : Said layer boundary, a method of operating an image processing apparatus according to claim 7, characterized in that it comprises a retinal pigment epithelium boundary. Calculating means, a method of operating an image processing apparatus according to claim 7 or claim 8, characterized in that calculation step further has a for calculating the risk information of the subject's eye based on the detected lamina cribrosa regions . The method of operating an image processing apparatus according to claim 9, further comprising a display control step of displaying the risk information on a display unit. 11. The measurement method according to claim 7, further comprising a measurement step of measuring an analysis value related to a range of the sieve plate region in a depth direction of the fundus by analyzing the detected sieve plate region. The operation method of the image processing apparatus of any one of Claims 1. The method of operating an image processing apparatus according to claim 11, wherein in the measuring step, the thickness of the detected sieve plate region is measured as the analysis value.   The program for functioning a computer as each means of the image processing apparatus of any one of Claims 1 thru | or 6.

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