Conference Paper2025

A Two-Stage Deep Learning Framework for Liver and Hepatocellular Carcinoma Segmentation on MRI

Md Zafor Iqbal, Siam Tahsin Bhuiyan, Riyadul Islam, Halima Khatun, Samiul Karim Mazumder, Rashedur Rahman, Ashraful Islam, Saadia Binte Alam

2025 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)

IEEE, pp. 359-364

  • Good Health and Well-being — Relevance: 41%
DOI: 10.1109/becithcon69222.2025.11504267URL

Abstract

Automated segmentation of the liver and hepatocellular carcinoma (HCC) from Magnetic Resonance Imaging (MRI) is critical for diagnosis and treatment planning. In order to create a performance baseline on the publicly available LiverHCCSeg dataset, this work creates and assesses a two-stage segmentation pipeline based on deep learning. Our approach uses a U-Net model to segment the liver in its entirety first, and then a second U-Net model that segments the tumor inside a cropped region of interest using the projected liver mask. The model demonstrated a consistent test Dice Similarity Coefficient (DSC) of 0.925 for liver segmentation, indicating robust and notable accuracy performance. However, the tumor segmentation task was challenging, with an initial test DSC of only 0.312 on the first random data split. Further iterative curation of the train-test split was required to improve the final test DSC to 0.473, indicating the influence of dataset bias in small cohorts. This work highlights the difficulties of tumor segmentation with minimal data and offers an essential baseline for further studies on the LiverHCCSeg dataset

Keywords

  • U-Net
  • Hepatocellular carcinoma
  • tumor segmentation
  • liver segmentation
  • Magnetic resonance imaging
  • two-stage segmentation
  • train-test split
  • data bias
  • Dice coefficient
  • small cohorts

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Riyadul Islam
Riyadul Islam

Riyadul Islam

  • Intern, CCDS

Bachelors

Daffodil International University

Published at

Scientific Reports

Halima Khatun
Halima Khatun

Halima Khatun

  • Research Assistant, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

References

  1. 1.Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. (2016). Deep Residual Learning for Image Recognition. , 770–778[10.1109/cvpr.2016.90]
  2. 2.Olaf Ronneberger, Philipp Fischer, Thomas Brox. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture notes in computer science, 234–241[10.1007/978-3-319-24574-4_28]
  3. 3.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]
  4. 4.Harriet Rumgay, Melina Arnold, Jacques Ferlay, Olufunmilayo Lesi, Citadel J. Cabasag, Jérôme Vignat, Mathieu Laversanne, Katherine A. McGlynn, Isabelle Soerjomataram. (2022). Global burden of primary liver cancer in 2020 and predictions to 2040. Journal of Hepatology, 77(6), 1598–1606[10.1016/j.jhep.2022.08.021]
  5. 5.Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivanti, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yue, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren. (2022). The Liver Tumor Segmentation Benchmark (LiTS). Medical Image Analysis, 84, 102680[10.1016/j.media.2022.102680]
  6. 6.Xuxin Chen, Ximin Wang, Ke Zhang, Kar-Ming Fung, Theresa C. Thai, Kathleen Moore, Robert S. Mannel, Hong Liu, Bin Zheng, Yuchen Qiu. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444[10.1016/j.media.2022.102444]
  7. 7.S. Suganyadevi, V. Seethalakshmi, K. Balasamy. (2021). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19–38[10.1007/s13735-021-00218-1]
  8. 8.Qianru Li, Maomao Cao, Lin Lei, Fan Yang, He Li, Xinxin Yan, Siyi He, Shaoli Zhang, Yi Teng, Changfa Xia, Wanqing Chen. (2022). Burden of liver cancer: From epidemiology to prevention. Chinese Journal of Cancer Research, 34(3), 554–566[10.21147/j.issn.1000-9604.2022.06.02]
  9. 9.Bagher Sistaninejhad, Habib Rasi, Parisa Nayeri. (2023). A Review Paper about Deep Learning for Medical Image Analysis. Computational and Mathematical Methods in Medicine, 2023(1), 7091301[10.1155/2023/7091301]
  10. 10.Evangelos Chartampilas, Vasileios Rafailidis, Vivian Georgopoulou, Georgios Kalarakis, Adam Hatzidakis, Panos Prassopoulos. (2022). Current Imaging Diagnosis of Hepatocellular Carcinoma. Cancers, 14(16), 3997[10.3390/cancers14163997]
  11. 11.Anirudh Ashok Aatresh, Kumar Alabhya, Shyam Lal, Jyoti Kini, PU Prakash Saxena. (2021). LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. International Journal of Computer Assisted Radiology and Surgery, 16(9), 1549–1563[10.1007/s11548-021-02410-4]
  12. 12.Jing Wang, Yanyang Peng, Shi Jing, Lujun Han, Tian Li, Junpeng Luo. (2023). A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++. BMC Cancer, 23(1), 1060[10.1186/s12885-023-11432-x]
  13. 13.Moritz Gross, Steffen Huber, Sandeep Arora, Tal Ze’evi, Stefan P. Haider, Ahmet S. Kucukkaya, Simon Iseke, Tom Niklas Kuhn, Bernhard Gebauer, Florian Michallek, Marc Dewey, Valérie Vilgrain, Riccardo Sartoris, Maxime Ronot, Ariel Jaffe, Mario Strazzabosco, Julius Chapiro, John A. Onofrey. (2024). Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics. European Radiology, 34(8), 5056–5065[10.1007/s00330-023-10495-5]
  14. 14.Maryam Khoshkhabar, Saeed Meshgini, Reza Afrouzian, Sebelan Danishvar. (2023). Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network. Sensors, 23(17), 7561[10.3390/s23177561]
  15. 15.Tianying Zheng, Yajing Zhu, Hanyu Jiang, Chongtu Yang, Yuxiang Ye, Mustafa R. Bashir, Chenhui Li, Liling Long, Shishi Luo, Bin Song, Yinan Chen, Yinan Chen, Yidi Chen, Yidi Chen. (2025). MRI ‐Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC. Liver International, 45(3), e16205[10.1111/liv.16205]
  16. 16.Lu Meng, Qianqian Zhang, Sihang Bu. (2021). Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network. Diagnostics, 11(10), 1806[10.3390/diagnostics11101806]
  17. 17.Ya Ma, Yue Gong, QingTao Qiu, Changsheng Ma, Shuang Yu. (2024). Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma. BMC Cancer, 24(1), 363[10.1186/s12885-024-12109-9]
  18. 18.Zhendong Song, Huiming Wu, Wei Chen, Adam Slowik. (2024). Improving automatic segmentation of liver tumor images using a deep learning model. Heliyon, 10(7), e28538[10.1016/j.heliyon.2024.e28538]
  19. 19.Maryam Fallahpoor, Dan Nguyen, Ehsan Montahaei, Ali Hosseini, Shahram Nikbakhtian, Maryam Naseri, Faeze Salahshour, Saeed Farzanefar, Mehrshad Abbasi. (2024). Segmentation of liver and liver lesions using deep learning. Physical and Engineering Sciences in Medicine, 47(2), 611–619[10.1007/s13246-024-01390-4]
  20. 20.Jiahao Liao, Hongyuan Wang, Hanjie Gu, Yinghui Cai. (2024). Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks. PLoS ONE, 19(12), e0312105[10.1371/journal.pone.0312105]
  21. 21.Sidra Gul, Muhammad Salman Khan, Md Sakib Abrar Hossain, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon. (2024). A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework. Diagnostics, 14(23), 2761[10.3390/diagnostics14232761]
  22. 22.Dipanjan Moitra, Rakesh Kr. Mandal. (2022). Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. Multimedia Tools and Applications, 81(7), 10279–10297[10.1007/s11042-022-12229-z]
  23. 23.Vivek Yadav, Amine Geahchan, Valentin Fauveau, Kazuya Yasokawa, Bachir Taouli, Hayit Greenspan. (2025). Automated hepatocellular carcinoma detection and segmentation in abbreviated MRI using vision transformers. , 66[10.1117/12.3047021]
  24. 24.Moritz Gross, Sandeep Arora, Steffen Huber, Ahmet S. Kücükkaya, John A. Onofrey. (2023). LiverHccSeg: A publicly available multiphasic MRI dataset with liver and HCC tumor segmentations and inter-rater agreement analysis. Data in Brief, 51, 109662[10.1016/j.dib.2023.109662]
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