Format

Send to

Choose Destination
Comput Biol Med. 2018 Oct 1;101:153-162. doi: 10.1016/j.compbiomed.2018.08.018. Epub 2018 Aug 18.

Robust liver vessel extraction using 3D U-Net with variant dice loss function.

Author information

1
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, China. Electronic address: q-huang12@mails.tsinghua.edu.cn.
2
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, China. Electronic address: sjf16@mails.tsinghua.edu.cn.
3
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, China. Electronic address: dinghui@tsinghua.edu.cn.
4
Department of Interventional Radiology, Peking University Cancer Hospital & Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Beijing, 100142, China. Electronic address: tigat@126.com.
5
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing, 100084, China. Electronic address: wgz-dea@tsinghua.edu.cn.

Abstract

PURPOSE:

Liver vessel extraction from CT images is essential in liver surgical planning. Liver vessel segmentation is difficult due to the complex vessel structures, and even expert manual annotations contain unlabeled vessels. This paper presents an automatic liver vessel extraction method using deep convolutional network and studies the impact of incomplete data annotation on segmentation accuracy evaluation.

METHODS:

We select the 3D U-Net and use data augmentation for accurate liver vessel extraction with few training samples and incomplete labeling. To deal with high imbalance between foreground (liver vessel) and background (liver) classes but also increase segmentation accuracy, a loss function based on a variant of the dice coefficient is proposed to increase the penalties for misclassified voxels. We include unlabeled liver vessels extracted by our method in the expert manual annotations, with a specialist's visual inspection for refinement, and compare the evaluations before and after the procedure.

RESULTS:

Experiments were performed on the public datasets Sliver07 and 3Dircadb as well as local clinical datasets. The average dice and sensitivity for the 3Dircadb dataset were 67.5% and 74.3%, respectively, prior to annotation refinement, as compared with 75.3% and 76.7% after refinement.

CONCLUSIONS:

The proposed method is automatic, accurate and robust for liver vessel extraction with high noise and varied vessel structures. It can be used for liver surgery planning and rough annotation of new datasets. The evaluation difference based on some benchmarks, and their refined results, showed that the quality of annotation should be further considered for supervised learning methods.

KEYWORDS:

3D U-Net; Annotation quality; Liver vessel extraction; Refined manual expert annotations; Variant dice loss function

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center