Paper Info
Title
Deep Learning To Segment Pelvic Bones: Large-Scale Ct Datasets And Baseline Models
Abstract
Purpose: Pelvic bone segmentation in CT has always been an essential step in clinical diagnosis and surgery planning of pelvic bone diseases. Existing methods for pelvic bone segmentation are either hand-crafted or semi-automatic and achieve limited accuracy when dealing with image appearance variations due to the multi-site domain shift, the presence of contrasted vessels, coprolith and chyme, bone fractures, low dose, metal artifacts, etc. Due to the lack of a large-scale pelvic CT dataset with annotations, deep learning methods are not fully explored.Methods: In this paper, we aim to bridge the data gap by curating a large pelvic CT dataset pooled from multiple sources, including 1184 CT volumes with a variety of appearance variations. Then, we propose for the first time, to the best of our knowledge, to learn a deep multi-class network for segmenting lumbar spine, sacrum, left hip, and right hip, from multiple-domain images simultaneously to obtain more effective and robust feature representations. Finally, we introduce a post-processor based on the signed distance function (SDF).Results: Extensive experiments on our dataset demonstrate the effectiveness of our automatic method, achieving an average Dice of 0.987 for a metal-free volume. SDF post-processor yields a decrease of 15.1% in Hausdorff distance compared with traditional post-processor.Conclusion: We believe this large-scale dataset will promote the development of the whole community and open source the images, annotations, codes, and trained baseline models at https://github.com/ICT-MIRACLE-lab/CTPelvic1K.
Year
DOI
Venue
2021
10.1007/s11548-021-02363-8
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
Keywords
DocType
Volume
CT dataset, Pelvic segmentation, Deep learning, SDF post-processing
Journal
16
Issue
ISSN
Citations 
5
1861-6410
0
PageRank 
References 
Authors
0.34
18
12
Name
Order
Citations
PageRank
Pengbo Liu100.34
Hu Han275240.02
Yuanqi Du301.01
Heqin Zhu400.34
Yinhao Li500.34
Fengshou Gu62323.43
Honghu Xiao700.34
Jun Li852.37
Chunpeng Zhao900.68
Xiao Li1000.68
Xinbao Wu1100.68
Zhou S. Kevin1247441.40