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

TL;DR

This paper introduces Cycle Prototype Network, a weakly supervised framework for 3D renal compartment segmentation on CT images, addressing boundary ambiguity and anatomy variation with novel learning modules.

Contribution

It proposes three innovations: Cycle Prototype Learning, Bayes Weakly Supervised Module, and Fine Decoding Feature Extractor, enhancing robustness and accuracy with minimal labeled data.

Findings

Achieves Dice score of 79.1% with only four labeled images.

Outperforms typical prototype models by about 20%.

Demonstrates effective error correction and fine segmentation in challenging 3D CT images.

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.