Related papers: CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images

CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images

URL: http://arxiv.org/abs/2108.06669v1
Date: Sun, 15 Aug 2021 06:54:38 GMT
Title: CPNet: Cycle Prototype Network for Weakly-supervised 3D Renal Compartments Segmentation on CT Images
Authors: Song Wang, Yuting He, Youyong Kong, Xiaomei Zhu, Shaobo Zhang, Pengfei Shao, Jean-Louis Dillenseger, Jean-Louis Coatrieux, Shuo Li, Guanyu Yang
Abstract summary: Renal compartment segmentation on CT images targets on extracting the 3D structure of renal compartments from abdominal CTA images. We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation. Our model achieves 79.1% and 78.7% with only four labeled images, achieving a significant improvement by about 20% than typical prototype model PANet.
Score: 14.756502627336305
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Renal compartment segmentation on CT images targets on extracting the 3D structure of renal compartments from abdominal CTA images and is of great significance to the diagnosis and treatment for kidney diseases. However, due to the unclear compartment boundary, thin compartment structure and large anatomy variation of 3D kidney CT images, deep-learning based renal compartment segmentation is a challenging task. We propose a novel weakly supervised learning framework, Cycle Prototype Network, for 3D renal compartment segmentation. It has three innovations: 1) A Cycle Prototype Learning (CPL) is proposed to learn consistency for generalization. It learns from pseudo labels through the forward process and learns consistency regularization through the reverse process. The two processes make the model robust to noise and label-efficient. 2) We propose a Bayes Weakly Supervised Module (BWSM) based on cross-period prior knowledge. It learns prior knowledge from cross-period unlabeled data and perform error correction automatically, thus generates accurate pseudo labels. 3) We present a Fine Decoding Feature Extractor (FDFE) for fine-grained feature extraction. It combines global morphology information and local detail information to obtain feature maps with sharp detail, so the model will achieve fine segmentation on thin structures. Our model achieves Dice of 79.1% and 78.7% with only four labeled images, achieving a significant improvement by about 20% than typical prototype model PANet.

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