Paper Info
Title
CyCoSeg: A Cyclic Collaborative Framework for Automated Medical Image Segmentation
Abstract
Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CyCoSeg</i> . The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans.
Year
DOI
Venue
2022
10.1109/TPAMI.2021.3113077
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keywords
DocType
Volume
Segmentation,image processing and computer vision,semantic networks,machine learning
Journal
44
Issue
ISSN
Citations 
11
0162-8828
0
PageRank 
References 
Authors
0.34
35
3
Name
Order
Citations
PageRank
Daniela Medley111.37
Carlos Santiago2175.71
Jacinto C. Nascimento339640.94