Submit to Algorithms Review for Algorithms Propose a Special Issue

Journal Menu

Journal Browser

Algorithms for Biomedical Image Analysis and Processing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 30722

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special issue Algorithms for Biomedical Image Analysis and Processing book cover image

Share This Special Issue

Special Issue Editors

Dr. Lucia Maddalena

E-Mail Website
Guest Editor

Institute for High-Performance Computing and Networking, National Research Council of Italy, via P. Castellino, 111, I-80131 Naples, Italy
Interests: computational data science; image processing; omics and imaging data integration
Special Issues, Collections and Topics in MDPI journals

Dr. Laura Antonelli

E-Mail Website
Guest Editor

Institute for High Performance and Computing, National Research Council of Italy, via P. Castellino, 111, I-80131 Naples, Italy
Interests: numerical methods; image processing; high-performance scientific computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging is a broad field concerning image capture for diagnostic and therapeutic purposes. Biomedical imaging technologies utilize X-rays (CT scans), magnetism (MRI), sound (ultrasound), radioactive pharmaceuticals (nuclear medicine: SPECT, PET), or light (endoscopy, OCT). Algorithms for processing and analyzing biomedical images are commonly used to visualize anatomical structures or assess the functionality of human organs, point out pathological regions, analyze biological and metabolic processes, set therapy plans, and carry out image-guided surgery. At a different scale, microscopy images are generally produced using light microscopes, which provide structural and temporal information about biological specimens. In the most widely used light microscopy techniques, light is transmitted from a source on the opposite side of the specimen to the objective lens. By contrast, fluorescence microscopy uses the reflected light of the specimen. Microscopy imaging requires methods for quantitative, unbiased, and reproducible extraction of meaningful measurements to quantify morphological properties and investigate intra- and intercellular dynamics. New technologies have been developed to address this need, such as microscopy-based screening, sequencing, and imaging, with automated analysis (including high-throughput screening and high-content screening), where basic image processing algorithms (e.g., denoising and segmentation) are fundamental tasks.

The large number of applications that rely on biomedical images increases the demand for efficient, accurate, and reliable algorithms for biomedical image processing and analysis, especially with the rising complexity of imaging technologies and the huge number of images to be processed.

This Special Issue aims to bring together both original research articles and topical reviews on algorithms for biomedical image processing and analysis techniques. Some basic techniques include deblurring, noise cleaning, filtering, 3D reconstruction from projection, segmentation, etc.

Submissions are welcome for algorithms based both on traditional approaches and on new machine learning techniques. Potential topics include but are not limited to:

Medical image analysis and processing;
Microscopy and histology image analysis and processing;
Computer-aided detection;
Computer-aided diagnosis;
Imaging biomarkers;
Reconstruction in emission tomography;
Computerized cell tracking;
Machine and deep learning for biomedical imaging;
Methods for combined imaging technologies.

Dr. Lucia Maddalena
Dr. Laura Antonelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

deblurring
denoising
segmentation
classification
detection
tracking
lineage

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (11 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Editorial

Jump to: Research, Review

4 pages, 173 KiB

Open AccessEditorial

Special Issue on “Algorithms for Biomedical Image Analysis and Processing”

by Laura Antonelli and Lucia Maddalena

Algorithms 2023, 16(12), 544; https://doi.org/10.3390/a16120544 - 28 Nov 2023

Viewed by 1336

Abstract

Biomedical imaging is a broad field concerning image capture for diagnostic and therapeutic purposes [...] Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

Research

Jump to: Editorial, Review

16 pages, 2021 KiB

Open AccessArticle

Clinical Validation of a New Enhanced Stent Imaging Method

by Chadi Ghafari, Khalil Houissa, Jo Dens, Claudiu Ungureanu, Peter Kayaert, Cyril Constant and Stéphane Carlier

Algorithms 2023, 16(6), 276; https://doi.org/10.3390/a16060276 - 30 May 2023

Cited by 1 | Viewed by 1730

Abstract

(1) Background: Stent underexpansion is the main cause of stent thrombosis and restenosis. Coronary angiography has limitations in the assessment of stent expansion. Enhanced stent imaging (ESI) methods allow a detailed visualization of stent deployment. We qualitatively compare image results from two ESI system vendors (StentBoost™ (SB) and CAAS StentEnhancer™ (SE)) and report quantitative results of deployed stents diameters by quantitative coronary angiography (QCA) and by SE. (2) Methods: The ESI systems from SB and SE were compared and graded by two blinded observers for different characteristics: 1 visualization of the proximal and distal edges of the stents; 2 visualization of the stent struts; 3 presence of underexpansion and 4 calcifications. Stent diameters were quantitatively measured using dedicated QCA and SE software and compared to chart diameters according to the pressure of implantation. (3) Results: A total of 249 ESI sequences were qualitatively compared. Inter-observer variability was noted for strut visibility and total scores. Inter-observer agreement was found for the assessment of proximal stent edge and stent underexpansion. The predicted chart diameters were 0.31 ± 0.30 mm larger than SE diameters (p < 0.05). Stent diameters by SE after post-dilatation were 0.47 ± 0.31 mm smaller than the post-dilation balloon diameter (p < 0.05). SE-derived diameters significantly differed from QCA; by Bland–Altman analysis the bias was −0.37 ± 0.42 mm (p < 0.001). (4) Conclusions: SE provides an enhanced visualization and allows precise quantitative assessment of stent expansion without the limitations of QCA when overlapping coronary side branches are present. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

Figure 1
On the top, comparison between the original angiographic image (left), StentBoost (middle) and StentEnhancer (right) and the region where the noise was computed (red square) and the signal (blue region of interest around the stent). Bottom, original frame (left) and StentBoost (middle) and StentEnhancer (right) results where an underexpansion can be detected from the important calcifications outside of the stent. SNR in original image is 2.8, and, respectively, 5.7 and 7.7 for the SB and SE images (in area around stent). Full article ">Figure 2
Underexpansion images before and after post-dilation (arrows). (A) StentBoost image before post-dilation; (B) StentEnhancer image before post-dilation; (C) StentBoost image after post-dilation; (D) StentEnhancer image after post-dilation. Full article ">Figure 3
Linear regression between SNRs of StentBoost and StentEnhancer on 53 different frames. Full article ">Figure 4
Quantitative analysis of an implanted proximal left anterior descending stent. Panel (A): quantitative coronary angiography analysis; Panel (B): StentEnhancer quantitative analysis post-implantation in two orthogonal views; Panel (C): StentEnhancer quantitative analysis after post-dilation. Stent expansion could be evaluated visually with clear stent struts and minimal calcifications. The pixels were transformed into millimeters using the calibration of the image. Note the presence of a second stent implanted in the ramus intermedius. Panel (D): Intravascular images at the minimal cross-sectional area in the stent after implantation (left) and after post-dilatation (right). Full article ">Figure 5
Bland-Altman analysis comparing QCA and SE. Red line: mean; green lines: 1.96 × SD; blue lines: 95%CI; circles: individual measurements. MSD: minimal stent diameter; SE: StentEnhancer; SD: standard deviation; QCA: quantitative coronary angiography. Full article ">

23 pages, 2417 KiB

Open AccessArticle

A Hybrid Direct Search and Model-Based Derivative-Free Optimization Method with Dynamic Decision Processing and Application in Solid-Tank Design

by Zhongda Huang, Andy Ogilvy, Steve Collins, Warren Hare, Michelle Hilts and Andrew Jirasek

Algorithms 2023, 16(2), 92; https://doi.org/10.3390/a16020092 - 7 Feb 2023

Viewed by 1318

Abstract

A derivative-free optimization (DFO) method is an optimization method that does not make use of derivative information in order to find the optimal solution. It is advantageous for solving real-world problems in which the only information available about the objective function is the output for a specific input. In this paper, we develop the framework for a DFO method called the DQL method. It is designed to be a versatile hybrid method capable of performing direct search, quadratic-model search, and line search all in the same method. We develop and test a series of different strategies within this framework. The benchmark results indicate that each of these strategies has distinct advantages and that there is no clear winner in the overall performance among efficiency and robustness. We develop the Smart DQL method by allowing the method to determine the optimal search strategies in various circumstances. The Smart DQL method is applied to a problem of solid-tank design for 3D radiation dosimetry provided by the UBCO (University of British Columbia—Okanagan) 3D Radiation Dosimetry Research Group. Given the limited evaluation budget, the Smart DQL method produces high-quality solutions. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

14 pages, 356 KiB

Open AccessArticle

Biomedical Image Classification via Dynamically Early Stopped Artificial Neural Network

by Giorgia Franchini, Micaela Verucchi, Ambra Catozzi, Federica Porta and Marco Prato

Algorithms 2022, 15(10), 386; https://doi.org/10.3390/a15100386 - 20 Oct 2022

Viewed by 1973

Abstract

It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. These data can be exploited to study diseases and their evolution in a deeper way or to predict their onsets. In particular, image classification represents one of the main problems in the biomedical imaging context. Due to the data complexity, biomedical image classification can be carried out by trainable mathematical models, such as artificial neural networks. When employing a neural network, one of the main challenges is to determine the optimal duration of the training phase to achieve the best performance. This paper introduces a new adaptive early stopping technique to set the optimal training time based on dynamic selection strategies to fix the learning rate and the mini-batch size of the stochastic gradient method exploited as the optimizer. The numerical experiments, carried out on different artificial neural networks for image classification, show that the developed adaptive early stopping procedure leads to the same literature performance while finalizing the training in fewer epochs. The numerical examples have been performed on the CIFAR100 dataset and on two distinct MedMNIST2D datasets which are the large-scale lightweight benchmark for biomedical image classification. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

15 pages, 6471 KiB

Open AccessArticle

Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images

by Choirul Anam, Ariij Naufal, Heri Sutanto, Kusworo Adi and Geoff Dougherty

Algorithms 2022, 15(10), 374; https://doi.org/10.3390/a15100374 - 13 Oct 2022

Cited by 6 | Viewed by 2551

Abstract

A bilateral filter is a non-linear denoising algorithm that can reduce noise while preserving the edges. This study explores the characteristics of a bilateral filter in changing the noise and texture within computed tomography (CT) images in an iterative implementation. We collected images of a homogeneous Neusoft phantom scanned with tube currents of 77, 154, and 231 mAs. The images for each tube current were filtered five times with a configuration of sigma space (σ_d) = 2 pixels, sigma intensity (σ_r) = noise level, and a kernel of 5 × 5 pixels. To observe the noise texture in each filter iteration, the noise power spectrum (NPS) was obtained for the five slices of each dataset and averaged to generate a stable curve. The modulation-transfer function (MTF) was also measured from the original and the filtered images. Tests on an anthropomorphic phantom image were carried out to observe their impact on clinical scenarios. Noise measurements and visual observations of edge sharpness were performed on this image. Our results showed that the bilateral filter was effective in suppressing noise at high frequencies, which is confirmed by the sloping NPS curve for different tube currents. The peak frequency was shifted from about 0.2 to about 0.1 mm⁻¹ for all tube currents, and the noise magnitude was reduced by more than 50% compared to the original images. The spatial resolution does not change with the number of iterations of the filter, which is confirmed by the constant values of MTF50 and MTF10. The test results on the anthropomorphic phantom image show a similar pattern, with noise reduced by up to 60% and object edges remaining sharp. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

16 pages, 1677 KiB

Open AccessArticle

Cancer Identification in Walker 256 Tumor Model Exploring Texture Properties Taken from Microphotograph of Rats Liver

by Mateus F. T. Carvalho, Sergio A. Silva, Jr., Carla Cristina O. Bernardo, Franklin César Flores, Juliana Vanessa C. M. Perles, Jacqueline Nelisis Zanoni and Yandre M. G. Costa

Algorithms 2022, 15(8), 268; https://doi.org/10.3390/a15080268 - 31 Jul 2022

Cited by 2 | Viewed by 2318

Abstract

Recent studies have been evaluating the presence of patterns associated with the occurrence of cancer in different types of tissue present in the individual affected by the disease. In this article, we describe preliminary results for the automatic detection of cancer (Walker 256 tumor) in laboratory animals using preclinical microphotograph images of the subject’s liver tissue. In the proposed approach, two different types of descriptors were explored to capture texture properties from the images, and we also evaluated the complementarity between them. The first texture descriptor experimented is the widely known Local Phase Quantization (LPQ), which is a descriptor based on spectral information. The second one is built by the application of a granulometry given by a family of morphological filters. For classification, we have evaluated the algorithms Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Logistic Regression. Experiments carried out on a carefully curated dataset developed by the Enteric Neural Plasticity Laboratory of the State University of Maringá showed that both texture descriptors provide good results in this scenario. The accuracy rates obtained using the SVM classifier were 96.67% for the texture operator based on granulometry and 91.16% for the LPQ operator. The dataset was made available also as a contribution of this work. In addition, it is important to remark that the best overall result was obtained by combining classifiers created using both descriptors in a late fusion strategy, achieving an accuracy of 99.16%. The results obtained show that it is possible to automatically perform the identification of cancer in laboratory animals by exploring texture properties found on the tissue taken from the liver. Moreover, we observed a high level of complementarity between the classifiers created using LPQ and granulometry properties in the application addressed here. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

Figure 1
Liver microphotograph from the control group (C). Full article ">Figure 2
Liver microphotograph from the Walker 256 tumor group (TW). Full article ">Figure 3
Pixels affected by application of a opening by reconstruction and of a closing by reconstruction, using a disk structuring element with radius one. Full article ">Figure 4
GBD generated by an opening by reconstruction. A disk-structuring element with radius <math display="inline"><semantics> <mi>λ</mi> </semantics></math> was used for each <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. Full article ">Figure 5
Binary size distributions from an area opening granulometry, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mn>50</mn> <mo>]</mo> </mrow> </semantics></math>. One size distribution was computed for each image from the dataset introduced in <a href="#sec3-algorithms-15-00268" class="html-sec">Section 3</a>. Full article ">Figure 6
Binary size distributions from an area closing granulometry, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mo>⋯</mo> <mo>,</mo> <mn>50</mn> <mo>]</mo> </mrow> </semantics></math>. One size distribution was computed for each image from the dataset introduced in <a href="#sec3-algorithms-15-00268" class="html-sec">Section 3</a>. Full article ">Figure 7
General overview of the methodology used for classification. Full article ">

13 pages, 1921 KiB

Open AccessArticle

Semi-Automatic Multiparametric MR Imaging Classification Using Novel Image Input Sequences and 3D Convolutional Neural Networks

by Bochong Li, Ryo Oka, Ping Xuan, Yuichiro Yoshimura and Toshiya Nakaguchi

Algorithms 2022, 15(7), 248; https://doi.org/10.3390/a15070248 - 18 Jul 2022

Cited by 2 | Viewed by 2146

Abstract

The role of multi-parametric magnetic resonance imaging (mp-MRI) is becoming increasingly important in the diagnosis of the clinical severity of prostate cancer (PCa). However, mp-MRI images usually contain several unaligned 3D sequences, such as DWI image sequences and T2-weighted image sequences, and there are many images among the entirety of 3D sequence images that do not contain cancerous tissue, which affects the accuracy of large-scale prostate cancer detection. Therefore, there is a great need for a method that uses accurate computer-aided detection of mp-MRI images and minimizes the influence of useless features. Our proposed PCa detection method is divided into three stages: (i) multimodal image alignment, (ii) automatic cropping of the sequence images to the entire prostate region, and, finally, (iii) combining multiple modal images of each patient into novel 3D sequences and using 3D convolutional neural networks to learn the newly composed 3D sequences with different modal alignments. We arrange the different modal methods to make the model fully learn the cancerous tissue features; then, we predict the clinical severity of PCa and generate a 3D cancer response map for the 3D sequence images from the last convolution layer of the network. The prediction results and 3D response map help to understand the features that the model focuses on during the process of 3D-CNN feature learning. We applied our method to Toho hospital prostate cancer patient data; the AUC (=0.85) results were significantly higher than those of other methods. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

Figure 1
The framework of the proposed method consists of four key steps: (1) rigid multiparameter (DWI, T2) image alignment, (2) prostate region cropping, and (3) building a new 3D image sequence for input into a 3D-CNN. Full article ">Figure 2
Examples of the alignment of DWI and T2 images are shown: (1) original T2 image, (2) original DWI image, and (3) aligned T2 image after being overlaid with the DWI image. Full article ">Figure 3
The figure shows the prostate detection and cropping procedure used in this paper. In the step of CNN-based prostate region detection, each rectangular box in the figure represents a feature map vector and shows the dimensional information of the feature map, the lower left corner of each feature map shows the length and width of the feature map, and the top shows the number of channels of the feature map. After this network, three output parameters can be obtained: the center coordinates (x, y) of the detected square region containing the prostate and the length of the square region L. Full article ">Figure 4
The 3D-CNN model was visualized for learning. The figure shows two cases; the first column of each case is the original image, the second column is the focus map obtained through calculation, and the third column is the heat map obtained through the 3D Grad-cam++ calculation. Full article ">

18 pages, 300 KiB

Open AccessArticle

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

by Igor D. Rodrigues, Emerson A. de Carvalho, Caio P. Santana and Guilherme S. Bastos

Algorithms 2022, 15(6), 195; https://doi.org/10.3390/a15060195 - 7 Jun 2022

Cited by 8 | Viewed by 3307

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

16 pages, 4962 KiB

Open AccessArticle

Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation

by Misgana Negassi, Diane Wagner and Alexander Reiterer

Algorithms 2022, 15(5), 165; https://doi.org/10.3390/a15050165 - 16 May 2022

Cited by 10 | Viewed by 3637

Abstract

Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. The existing work focuses on image classification and object detection, whereas we provide the first study on semantic image segmentation and introduce two new approaches: SmartAugment and SmartSamplingAugment. SmartAugment uses Bayesian Optimization to search a rich space of augmentation strategies and achieves new state-of-the-art performance in all semantic segmentation tasks we consider. SmartSamplingAugment, a simple parameter-free approach with a fixed augmentation strategy, competes in performance with the existing resource-intensive approaches and outperforms cheap state-of-the-art data augmentation methods. Furthermore, we analyze the impact, interaction, and importance of data augmentation hyperparameters and perform ablation studies, which confirm our design choices behind SmartAugment and SmartSamplingAugment. Lastly, we will provide our source code for reproducibility and to facilitate further research. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

Review

Jump to: Editorial, Research

8 pages, 1976 KiB

Open AccessReview

Polymer Models of Chromatin Imaging Data in Single Cells

by Mattia Conte, Andrea M. Chiariello, Alex Abraham, Simona Bianco, Andrea Esposito, Mario Nicodemi, Tommaso Matteuzzi and Francesca Vercellone

Algorithms 2022, 15(9), 330; https://doi.org/10.3390/a15090330 - 16 Sep 2022

Cited by 4 | Viewed by 1999

Abstract

Recent super-resolution imaging technologies enable tracing chromatin conformation with nanometer-scale precision at the single-cell level. They revealed, for example, that human chromosomes fold into a complex three-dimensional structure within the cell nucleus that is essential to establish biological activities, such as the regulation of the genes. Yet, to decode from imaging data the molecular mechanisms that shape the structure of the genome, quantitative methods are required. In this review, we consider models of polymer physics of chromosome folding that we benchmark against multiplexed FISH data available in human loci in IMR90 fibroblast cells. By combining polymer theory, numerical simulations and machine learning strategies, the predictions of the models are validated at the single-cell level, showing that chromosome structure is controlled by the interplay of distinct physical processes, such as active loop-extrusion and thermodynamic phase-separation. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Figure 1

Figure 1
The median spatial distance matrix of the IMR90 locus (Chr21: 28–30 Mb) from multiplexed FISH microscopy [<a href="#B15-algorithms-15-00330" class="html-bibr">15</a>] is compared against the corresponding in silico matrices of the considered polymer models [<a href="#B28-algorithms-15-00330" class="html-bibr">28</a>]. Both loop-extrusion (LE) and phase-separation based models (SBS and LE + SBS) well recapitulate the complex experimental pattern of contacts. Adapted from [<a href="#B28-algorithms-15-00330" class="html-bibr">28</a>]. Full article ">Figure 2
The genomic boundary probability function of the IMR90 locus is consistently recapitulated by the different models. Adapted from [<a href="#B28-algorithms-15-00330" class="html-bibr">28</a>]. Full article ">Figure 3
Example of experiment-model best-matching 3D structures, along with their corresponding single-molecule distance maps, as identified by the RMSD method. Adapted from [<a href="#B28-algorithms-15-00330" class="html-bibr">28</a>]. Full article ">Figure 4
Statistical significance of the RMSD analysis: (a) the RMSD distribution of the experiment-model best-matches (for each type of model) is statistically distinguishable from a control made of random pairs of imaged conformations; (b) less than 5% of the entries of the experiment-model best-match distributions are within the first 10% of the control. Adapted from [<a href="#B28-algorithms-15-00330" class="html-bibr">28</a>]. Full article ">

22 pages, 1059 KiB

Open AccessReview

Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-Free Microscopy Imaging

by Lucia Maddalena, Laura Antonelli, Alexandra Albu, Aroj Hada and Mario Rosario Guarracino

Algorithms 2022, 15(9), 313; https://doi.org/10.3390/a15090313 - 31 Aug 2022

Cited by 14 | Viewed by 4989

Abstract

Background: Time-lapse microscopy imaging is a key approach for an increasing number of biological and biomedical studies to observe the dynamic behavior of cells over time which helps quantify important data, such as the number of cells and their sizes, shapes, and dynamic interactions across time. Label-free imaging is an essential strategy for such studies as it ensures that native cell behavior remains uninfluenced by the recording process. Computer vision and machine/deep learning approaches have made significant progress in this area. Methods: In this review, we present an overview of methods, software, data, and evaluation metrics for the automatic analysis of label-free microscopy imaging. We aim to provide the interested reader with a unique source of information, with links for further detailed information. Results: We review the most recent methods for cell segmentation, event detection, and tracking. Moreover, we provide lists of publicly available software and datasets. Finally, we summarize the metrics most frequently adopted for evaluating the methods under exam. Conclusions: We provide hints on open challenges and future research directions. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

► Show Figures

Journal Menu

Journal Browser

Algorithms for Biomedical Image Analysis and Processing

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (11 papers)

Editorial

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI