CIRSeg: Coarse-to-Fine Intensity-Robust Liver Segmentation with Source-Free Continual Test-Time Adaptation

Ruoshi Xu; Mingqi Gao; Shengda Luo; Jingkun Chen

cs.CVOct 7, 2026

CIRSeg: Coarse-to-Fine Intensity-Robust Liver Segmentation with Source-Free Continual Test-Time Adaptation

Authors: Ruoshi Xu, Mingqi Gao, Shengda Luo, Jingkun Chen

Organizations: Chinese Medicine Guangdong Laboratory, Zhuhai, China · School of Computer Science, University of Sheffield, Sheffield, UK · School of Automation, Northwestern Polytechnical University, Xi’an, China

Abstract

Reliable liver segmentation in contrast-enhanced MRI is essential for quantitative hepatic assessment, treatment planning, and longitudinal disease monitoring. However, limited annotated data and scanner- or vendor-dependent intensity variations can cause overfitting and poor generalization to unseen acquisition domains. Moreover, simultaneously achieving robust global localization and precise boundary delineation remains challenging, while predictions may contain isolated false-positive regions outside the main liver component. To address these challenges, we propose CIRSeg, a coarse-to-fine, intensity-robust liver segmentation framework based on nnU-Netv2. CIRSeg combines 3D CutMix with stochastic intensity transfer using either Nyul augmentation or histogram matching to improve robustness to heterogeneous MRI intensities. Its cascaded architecture decouples low-resolution anatomical localization from full-resolution boundary refinement. At inference, source-free test-time adaptation based on confidence-filtered predictions and probability-prior regularization further improves robustness to out-of-distribution inputs. As a final deterministic post-processing step, largest connected component filtering removes isolated false-positive regions. On the CARE 2026 test set, CIRSeg achieves Dice scores of 97.13% and 97.93% on the in-domain and unseen-domain subsets, with corresponding HD95 values of 20.18 mm and 11.30 mm, respectively. These results demonstrate consistently accurate segmentation across both in-domain and unseen acquisition settings. The code is available at https://github.com/jingkunchen/MICCAI_CARE_2026

Figures & tables

Figure 1: Overall architecture of CIRSeg.

Figure 2: Visualization of histogram matching and Nyul augmentation. Each row shows a source volume, a randomly sampled reference volume, the histogram-matched result, and Nyul-augmented results with different transfer strengths $\alpha$ , together with their foreground intensity distributions.

Method	Dice $\uparrow$	HD95 $\downarrow$
CIRSeg (ours)	0.9595	5.29
CoSSeg-TTA Baseline	0.9445	7.74
nnU-Netv2	0.8973	26.95
Attention U-Net	0.8713	46.21
SegResNet	0.8704	32.19
3D U-Net	0.8537	37.07

Table 1: Main experimental comparison using five-fold cross-validation on the official training set.

Figure 3: Qualitative comparison on representative CARE 2026 validation cases. The last column shows CIRSeg predictions, which provide more complete liver coverage and no disconnected false-positive regions compared with previous methods.

Exp	LCC	CutMix	Composite Aug	Cascade	TTA	Dice $\uparrow$	HD95 $\downarrow$
Baseline						0.9693	24.69
A	$\surd$					0.9708	20.61
B	$\surd$	$\surd$				0.9702	19.18
C	$\surd$	$\surd$	$\surd$			0.9715	18.93
D	$\surd$	$\surd$	$\surd$	$\surd$		0.9713	18.31
E	$\surd$	$\surd$	$\surd$	$\surd$	$\surd$	0.9713	18.31

Table 2: Cumulative ablation results obtained through the official CARE 2026 evaluation website. The baseline is the reproduced CoSSeg-TTA framework described in Section 3.3 . LCC : largest connected component filtering; Composite Aug : composite intensity augmentation using histogram matching and Nyul augmentation; Cascade : cascaded nnU-Netv2 architecture; TTA : source-free continual test-time adaptation.

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