Abstract

We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural changes in the liver and can lead to serious clinical complications, making severity assessment important for disease monitoring and treatment planning. However, severity classification is challenging because imaging patterns are often subtle, spatially variable, and similar across adjacent stages. CirrGuide addresses this by explicitly linking localization with classification. A ResNet50 encoder with an Attention U-Net decoder first predicts a soft cirrhotic liver mask, which is then used as an anatomical prior in a ResNet50-based classification branch. This branch combines global multi-scale features with mask-guided attention-pooled regional features to classify Mild, Moderate, and Severe cirrhosis. On the official CirrMRI600+ T2-weighted (T2W) 2D split, CirrGuide achieves 89.83% Dice and 84.14% mIoU for segmentation, 69.58% accuracy and 61.55% macro F1-score for severity classification. Compared with segmentation-only, classification-only, and multi-task baselines, CirrGuide improves both localization and severity classification, demonstrating the benefit of using predicted cirrhotic liver masks as anatomical priors for cirrhosis analysis.

Links to Paper and Supplementary Materials

Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/MLMI_051.pdf

SharedIt Link: Not yet available

SpringerLink (DOI): Not yet available

Supplementary Material: Not Submitted

Link to Open Review

Open Review Page: https://openreview.net/forum?id=534YvAjOvL

BibTex

@InProceedings{RajMun_CirrGuide_MICCAISAT2026,
        author = { Raju, Muntaqim Ahmed AND Ma, Ruizhe},
        title = { { CirrGuide: A Deep Cascaded Framework for Liver Cirrhosis Segmentation and Severity Classification from T2-Weighted MRI } },
        booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
        year = {2026},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 17272},
        month = {pending},
        page = {pending}
}


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