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Dissecting Model Failures in Abdominal Aortic Aneurysm Segmentation through Explainability-Driven Analysis

Abu Noman Md Sakib, Merjulah Roby, Zijie Zhang, Satish Muluk, Mark K. Eskandari, Ender A. Finol

Abstract

Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the model looks is the primary training signal, and thus we propose an Explainable AI (XAI) guided encoder shaping framework. Our method computes a dense, attribution-based encoder focus map ("XAI field") from the final encoder block and uses it in two complementary ways: (i) we align the predicted probability mass to the XAI field to promote agreement between focus and output; and (ii) we route the field into a lightweight refinement pathway and a confidence prior that modulates logits at inference, suppressing distractors while preserving subtle structures. The objective terms serve only as control signals; the contribution is the integration of attribution guidance into representation and decoding. We evaluate clinically validated challenging cases curated for failure-prone scenarios. Compared to a base SAM setup, our implementation yields substantial improvements. The observed gains suggest that explicitly optimizing encoder focus via XAI guidance is a practical and effective principle for reliable segmentation in complex scenarios.

Dissecting Model Failures in Abdominal Aortic Aneurysm Segmentation through Explainability-Driven Analysis

Abstract

Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the model looks is the primary training signal, and thus we propose an Explainable AI (XAI) guided encoder shaping framework. Our method computes a dense, attribution-based encoder focus map ("XAI field") from the final encoder block and uses it in two complementary ways: (i) we align the predicted probability mass to the XAI field to promote agreement between focus and output; and (ii) we route the field into a lightweight refinement pathway and a confidence prior that modulates logits at inference, suppressing distractors while preserving subtle structures. The objective terms serve only as control signals; the contribution is the integration of attribution guidance into representation and decoding. We evaluate clinically validated challenging cases curated for failure-prone scenarios. Compared to a base SAM setup, our implementation yields substantial improvements. The observed gains suggest that explicitly optimizing encoder focus via XAI guidance is a practical and effective principle for reliable segmentation in complex scenarios.

Paper Structure

This paper contains 22 sections, 18 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. AAA Segmentation and Vascular Imaging
  4. Deep Segmentation Architectures
  5. Explainability and XAI-guided Learning
  6. Shape and Consistency Constraints
  7. Methods
  8. Problem Setup and Backbone
  9. Failure Analysis of Baseline
  10. Pairwise Consistency Classifier
  11. XAI Field and Focus Alignment
  12. Gradient-based XAI Field
  13. Probability Focus Alignment
  14. Anatomy-aware and Confidence Prior
  15. Experimental Results
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Performance comparison in complex outer wall AAA segmentation cases. Our XAI-SAM method significantly outperforms U-Net, U-Net++, MedSAM, and SAM measured by Dice score and HD95.
  • Figure 2: Overview of the proposed XAI-SAM framework. The left module introduces the pairwise consistency mechanism. The central module shows the SAM-based segmentation pipeline enhanced with pairwise consistency, probability-focus alignment, and anatomy-aware loss. The right module illustrates the XAI component.
  • Figure 3: Visualization of model performance on abdominal CT slices. From left to right: Ground Truth segmentation, Model Prediction, Attention Map highlighting regions of model focus, and Error Map indicating mismatched regions (red box) between Prediction and Ground Truth.
  • Figure 4: Visualization of model performance on abdominal CT slices. U-Net and U-Net++ frequently mis-localize or leak into adjacent vessels; MedSAM and SAM produce diffuse, unstable masks across slices; XAI-SAM accurately captures thin boundaries and maintains anatomical consistency across the full axial sequence (Red: Lumen. Green: Outer wall).