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

cs.CVMar 25, 2026

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

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

Organizations: University of Texas at San Antonio · Drexel University · Northwestern University

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.

Figures & tables

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).

Model	IoU (%) $\uparrow$	Dice (%) $\uparrow$	HD95 (mm) $\downarrow$
Outer Wall
U–Net [ 26 ]	68.81	77.21	14.98
U–Net++ [ 39 ]	68.23	78.13	22.15
MedSAM [ 17 ]	77.97	85.58	6.30
nnU-Net [ 8 ]	80.26	86.62	6.87
SAM baseline [ 12 ]	81.53	88.99	4.09

Table 1: General test set results. IoU and Dice are reported in percentages (↑ higher is better); HD95 in millimeters (↓ lower is better). Best results are in bold , second best are underlined .

Model	IoU (%) $\uparrow$	Dice (%) $\uparrow$	HD95 (mm) $\downarrow$
Outer Wall
U–Net [ 26 ]	65.14	74.15	17.98
U–Net++ [ 39 ]	65.58	76.20	23.74
MedSAM [ 17 ]	69.82	79.73	9.72
nnU-Net [ 8 ]	76.53	84.22	10.05
SAM baseline [ 12 ]	78.43	86.83	5.49

Table 2: Complex test set results. IoU and Dice are reported in percentages (↑ higher is better); HD95 in millimeters (↓ lower is better). Best results are in bold , second best are underlined .

Comp.			Outer Wall			Lumen
S	X	A	IoU	Dice	HD95	IoU	Dice	HD95
$\times$	$\times$	$\times$	78.4	86.8	5.4	82.1	89.6	2.6
$\times$	$\checkmark$	$\checkmark$	92.9	96.2	0.4	91.3	95.4	0.6
$\checkmark$	$\times$	$\checkmark$	93.3	96.4	0.6	90.0	94.6	1.0
$\checkmark$	$\checkmark$	$\times$	94.7	97.1	0.2	93.8	96.7	0.6
$\checkmark$	$\checkmark$	$\checkmark$	95.0	97.3	0.3	94.6	97.1	0.5

Table 3: Ablation study on the complex test set.

Image Segmentation

Image segmentation assigns pixels or voxels to objects and regions, supporting measurement, scene understanding, and decisions across image domains.

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In CT angiography, the accurate segmentation of abdominal aortic aneurysms (AAAs) is difficult due to large anatomical variability, low-contrast vessel boundaries, and the close proximity of organs whose intensities resemble vascular structures, often leading to false positives. To address these challenges, we propose an anatomy-aware segmentation framework that integrates organ exclusion masks derived from TotalSegmentator into the training process. These masks encode explicit anatomical priors by identifying non-vascular organsand penalizing aneurysm predictions within these regions, thereby guiding the U-Net to focus on the aorta and its pathological dilation while suppressing anatomically implausible predictions. Despite being trained on a relatively small dataset, the anatomy-aware model achieves high accuracy, substantially reduces false positives, and improves boundary consistency compared to a standard U-Net baseline. The results demonstrate that incorporating anatomical knowledge through exclusion masks provides an efficient mechanism to enhance robustness and generalization, enabling reliable AAA segmentation even with limited training data.

Abstract

Image Segmentation

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