A boundary-aware CNN with a dedicated edge stream and edge losses improves tumor-core segmentation on 2D slices of BraTS 2018 compared to U-Net and V-Net, though the evaluation has internal inconsistencies.
Deep Active Lesion Segmentation
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abstract
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework for that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy functional and a novel multiscale encoder-decoder CNN that learns an initialization probability map along with parameter maps for the ACM. We evaluate our lesion segmentation model on a new Multiorgan Lesion Segmentation (MLS) dataset that contains images of various organs, including brain, liver, and lung, across different imaging modalities---MR and CT. Our results demonstrate favorable performance compared to competing methods, especially for small training datasets. Source code : $\text{https://github.com/ahatamiz/dals}$
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End-to-End Boundary Aware Networks for Medical Image Segmentation
A boundary-aware CNN with a dedicated edge stream and edge losses improves tumor-core segmentation on 2D slices of BraTS 2018 compared to U-Net and V-Net, though the evaluation has internal inconsistencies.