Title
Semi-Supervised 3D Abdominal Multi-Organ Segmentation Via Deep Multi-Planar Co-Training
Abstract
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain. In comparison, massive unlabeled 3D CT volumes are usually easily accessible. Current mainstream works to address semi-supervised biomedical image segmentation problem are mostly graph-based. By contrast, deep network based semi-supervised learning methods have not drawn much attention in this field. In this work, we propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be divided into two folds: 1) The deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-labels and thus can help to train better segmentation networks. Experiments are done on our newly collected large dataset with 100 unlabeled cases as well as 210 labeled cases where 16 anatomical structures are manually annotated by four radiologists and confirmed by a senior expert. The results suggest that DMPCT significantly outperforms the fully supervised method by more than 4% especially when only a small set of annotations is used.
Year
DOI
Venue
2019
10.1109/WACV.2019.00020
2019 IEEE Winter Conference on Applications of Computer Vision (WACV)
Keywords
Field
DocType
Image segmentation,Three-dimensional displays,Biomedical imaging,Semisupervised learning,Data models,Two dimensional displays,Computed tomography
Data modeling,Computer vision,Pattern recognition,Segmentation,Medical imaging,Computer science,Co-training,Image segmentation,Planar,Artificial intelligence,Deep learning,Anatomical structures
Conference
ISSN
ISBN
Citations 
2472-6737
978-1-7281-1975-5
9
PageRank 
References 
Authors
0.50
0
7
Name
Order
Citations
PageRank
Yuyin Zhou19710.94
Yan Wang2203.10
Peng Tang3343.20
Song Bai453333.91
Wei Shen546426.02
Elliot K. Fishman616427.51
Alan L. Yuille7103391902.01