Paper Info
Title
CPNet: Cycle Prototype Network for Weakly-Supervised 3D Renal Compartments Segmentation on 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 extensive experiments demonstrated our great performance. 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 [16].
Year
DOI
Venue
2021
10.1007/978-3-030-87196-3_55
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT II
DocType
Volume
ISSN
Conference
12902
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
10
Name
Order
Citations
PageRank
Song Wang100.34
Yuting He254.45
Youyong Kong39615.23
Xiaomei Zhu400.34
Shaobo Zhang541.40
Pengfei Shao641.40
Jean-Louis Dillenseger710912.96
Jean-Louis Coatrieux800.34
Shuo Li901.01
Guanyu Yang102713.48