Abstract
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modelling. In this work we present a feature extraction scheme based on inception models for pixel classification tasks. We extract features under multi-scale and multi-layer schemes through convolutional operators. Layers of Fully Convolutional Network are later stacked on these feature extraction layers and trained end-to-end for the purpose of classification. We test our model on the DRIVE and STARE public data sets for the purpose of segmentation and centerline detection and it outperforms most existing hand crafted or deterministic feature schemes found in literature. We achieve an average maximum Dice of 0.85 on the DRIVE data set which outperforms the scores from the second human annotator of this data set. We also achieve an average maximum Dice of 0.85 and kappa of 0.84 on the STARE data set. Even though these datasets are only 2-D we also propose ways of extending this feature extraction scheme to handle 3-D datasets.
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Tetteh, G., Rempfler, M., Zimmer, C., Menze, B.H. (2017). Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_40
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DOI: https://doi.org/10.1007/978-3-319-67389-9_40
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