Yi-Qing Wang
ABSTRACT
Liver lesion segmentation is a key module for an automated liver disease diagnosis system. Numerous methods have been developed recently to produce accurate 3D binary lesion masks for CT scans. From the clinical perspective, it is thus important to be able to correctly parse these masks into separate lesion instances in order to enable downstream applications such as lesion tracking and characterization. For the lack of a better alternative, 3D connected component analysis is often used for this task, though it does not always work, especially in the presence of confluent lesions. In this paper, we propose a new method for parsing 3D binary lesion masks and an approach to evaluating its performance. We show that our method outperforms 3D connected component analysis on a large collection of annotated portal-venous phase studies.
- Serge Beucher and Fernand Meyer. 1993. The morphological approach to segmentation: the watershed transformation. Mathematical morphology in image processing 34 (1993), 433–481.Google Scholar
- Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, Samuel Kadoury, Tomasz K. Konopczynski, Miao Le, Chunming Li, Xiaomeng Li, Jana Lipková, John S. Lowengrub, Hans Meine, Jan Hendrik Moltz, Chris Pal, Marie Piraud, Xiaojuan Qi, Jin Qi, Markus Rempfler, Karsten Roth, Andrea Schenk, Anjany Sekuboyina, Ping Zhou, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Felix Grün, Georgios Kaissis, Fabian Lohöfer, Rickmer Braren, Julian Holch, Felix Hofmann, Wieland H. Sommer, Volker Heinemann, Colin Jacobs, Gabriel Efrain Humpire Mamani, Bram van Ginneken, Gabriel Chartrand, An Tang, Michal Drozdzal, Avi Ben-Cohen, Eyal Klang, Michal Marianne Amitai, Eli Konen, Hayit Greenspan, Johan Moreau, Alexandre Hostettler, Luc Soler, Refael Vivanti, Adi Szeskin, Naama Lev-Cohain, Jacob Sosna, Leo Joskowicz, and Bjoern H. Menze. 2019. The Liver Tumor Segmentation Benchmark (LiTS). CoRR abs/1901.04056(2019). arxiv:1901.04056http://arxiv.org/abs/1901.04056Google Scholar
- Grzegorz Chlebus, Andrea Schenk, Jan Moltz, Bram Ginneken, Hans Meine, and Horst Hahn. 2018. Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific Reports 8 (10 2018). https://doi.org/10.1038/s41598-018-33860-7Google ScholarCross Ref
- Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein. 2009. Introduction to algorithms. MIT press.Google Scholar
- Vicente Grau, AUJ Mewes, M Alcaniz, Ron Kikinis, and Simon K Warfield. 2004. Improved watershed transform for medical image segmentation using prior information. IEEE transactions on medical imaging 23, 4 (2004), 447–458.Google ScholarCross Ref
- Qiangguo Jin, Zhaopeng Meng, Changming Sun, Hui Cui, and Ran Su. 2020. RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans. Frontiers in Bioengineering and Biotechnology (2020), 1471.Google Scholar
- Xiaomeng Li, Hao Chen, Xiaojuan Qi, Qi Dou, Chi-Wing Fu, and Pheng-Ann Heng. 2018. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE transactions on medical imaging 37, 12 (2018), 2663–2674.Google Scholar
- Jiri Matousek and Bernd Gärtner. 2006. Understanding and using linear programming. Springer Science & Business Media.Google Scholar
- James Munkres. 1957. Algorithms for the assignment and transportation problems. Journal of the society for industrial and applied mathematics 5, 1(1957), 32–38.Google ScholarCross Ref
- Lei Song, Haoqian Wang, and Z Jane Wang. 2021. Bridging the gap between 2D and 3D contexts in CT volume for liver and tumor segmentation. IEEE Journal of Biomedical and Health Informatics 25, 9(2021), 3450–3459.Google ScholarCross Ref
- Youbao Tang, Jinzheng Cai, Ke Yan, Lingyun Huang, Guotong Xie, Jing Xiao, Jingjing Lu, Gigin Lin, and Le Lu. 2021. Weakly-supervised universal lesion segmentation with regional level set loss. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 515–525.Google ScholarDigital Library
- Youbao Tang, Yuxing Tang, Yingying Zhu, Jing Xiao, and Ronald M Summers. 2020. E2Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 512–522.Google Scholar
- Jianpeng Zhang, Yutong Xie, Yong Xia, and Chunhua Shen. 2021. DoDNet: Learning to segment multi-organ and tumors from multiple partially labeled datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1195–1204.Google ScholarCross Ref
- Jianpeng Zhang, Yutong Xie, Pingping Zhang, Hao Chen, Yong Xia, and Chunhua Shen. 2019. Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation.. In IJCAI, Vol. 19. 4271–4277.Google Scholar
Index Terms
- 3D Binary Lesion Mask Parsing
Recommendations
An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks
Highlights- A computational method for liver lesions segmentation in CT images is presented.
AbstractLiver cancer is one of the major causes of death by cancer. The early detection of lesions in the liver provides a better chance of treatment and cure of the disease. Computed tomography (CT) is one of the most used imaging techniques ...
Lungs segmentation by developing binary mask
FIT '09: Proceedings of the 7th International Conference on Frontiers of Information TechnologyLungs Segmentation from chest CT slices is a precursor for CAD applications. Most of the lungs segmentation methods are scanner dependent. We propose a fully automated machine independent method for segmenting lungs from CT images. The algorithm ...
Performance evaluation of breast lesion detection systems with expert delineations: a comparative investigation on mammographic images
AbstractPerformance of computerized diagnostic systems yearning to be approved by medical regulatory bodies must meet the expectations of human experts. Highly accurate lesion segmentation techniques have thus turned out to be an essential part for ...
Comments