The leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. The vertices are optimized by employing a differentiable neural renderer to create a raster image. The delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.
CITATION STYLE
Shaharabany, T., & Wolf, L. (2022). End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13435 LNCS, pp. 308–318). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16443-9_30
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