Conference Paper2025

A Cross-Modal Benchmark of Deep Learning Encoders for Automated Liver Segmentation

Md Zafor Iqbal, Redwan Rahman, Kazi Toushia Nahar, Siam Tahsin Bhuiyan, Ashraful Islam, Saadia Binte Alam, Rashedur Rahman, Md Ashraful Amin

2025 28th International Conference on Computer and Information Technology (ICCIT)

IEEE, pp. 3282-3287

  • Good Health and Well-being — Relevance: 76%
DOI: 10.1109/iccit68739.2025.11491768URL

Abstract

Numerous clinical applications rely on automated liver segmentation from medical images, but deep learning models' generalizability across many imaging modalities is still a major challenge. This study presents a detailed evaluation framework of U-Net-based models for liver segmentation by comparing four distinct pre-trained encoders: ResNet18, EfficientNetB3, DenseNet121, and MobileNetV2. The models were evaluated on three separate external validation datasets, which comprised both Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans, after being trained on the LiverHccSeg (MRI) dataset. The external validation datasets were 3D-IRCADb-01 (CT), CHAOS (MRI), and SLiver07 (CT). All models showed strong results on the main MRI test set, with the DenseNet121 encoder achieving the best Dice Similarity Coefficient (DSC) of 0.928. On the other hand, the EfficientNetB3 encoder showed better generalization during external validation, with a DSC of 0.825 for CT on the 3D-IRCADb-01 dataset and 0.852 for the MRI dataset.

Keywords

  • EfficientNetB3
  • liver segmentation
  • ResNet-18
  • MobileNetV2
  • U-Net
  • CT
  • chaos
  • DenseNet121
  • cross-modal generalization
  • Magnetic resonance imaging

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

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

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

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