CRISP: Constrained Refinement via Iterative Squeezing Process for Robust Medical Image Segmentation under Domain Shift

ReDi-FM: Frozen Foundation Model for Continual Test-Time Adaptation in Medical Image Segmentation

SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation

Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-free unsupervised domain adaptation (SFUDA) addresses this by adapting a pretrained model to an unlabeled target domain without requiring access to sensitive source data. We introduce a novel SFUDA framework built on Symmetrical Flow Matching, a unified generative model that segments an input image and synthesizes a source-like image from a mask within the same learned flow. By initializing inference from a domain-agnostic Gaussian origin, the model preserves structural consistency across domains and grounds predictions in learned anatomy rather than shifted texture statistics. Our pipeline leverages this symmetry to generate reliable pseudo-labels and corresponding source-like synthetic images from unlabeled target data, creating a generative replay buffer that anchors source knowledge during a generative self-training stage that fine-tunes on a joint set of real target and synthetic source-like images. We evaluate on abdominal multi-organ and cardiac segmentation, covering cross-modality MRI<->CT shifts, and multi-site prostate segmentation. Our approach outperforms SFUDA baselines and is competitive with conventional UDA methods.

MedSegBenchmarker: A Raw-Count-First Framework for Controlled 2D Medical Image Segmentation Benchmarks

Despite rapid advances in MIS, fair and reproducible comparisons of segmentation models remain challenging due to heterogeneous datasets, inconsistent evaluation protocols, and rapidly evolving architectures. In particular, comparisons often implicitly assume that model rankings are invariant to data partitioning, preprocessing, metric aggregation, uncertainty estimation, and computational constraints. The lack of extensible and unified evaluation frameworks further limits systematic investigation of new models, datasets, and training paradigms. We present MEDSEGBENCHMARKER (MSB), a configuration-driven framework for controlled benchmarking of 2D MIS. It integrates duplicate and near-duplicate image detection, group-aware data splitting, YAML study specifications, resumable training, hyperparameter optimization, cross-validation, and checkpoint-based evaluation. Rather than retaining only aggregate performance measures, MSB exports sample- and class-level pixel counts and predictions together with the evaluation context. These elementary artifacts enable post-hoc analyses without repeated inference. We demonstrate MSB in a case study involving three heterogeneous 2D datasets and multiple MIS and general-purpose vision models evaluated at 256- and 512-pixel input resolutions. Reaggregation of identical predictions changes the top-ranked architecture in three of six dataset-resolution settings, despite high rank correlations between aggregation strategies. Increasing input resolution produces model- and dataset-dependent performance gains and losses that must be considered alongside empirically measured inference complexity. These results show that seemingly minor choices in evaluation and experimental setup can affect benchmark conclusions. MSB, available at GitHub, provides a practical and extensible basis for making benchmark conditions and evaluation choices explicit and reproducible.

Robust unsupervised domain adaptation for medical image segmentation via frequency-conditioned graph diffusion

Cross-domain variability in medical imaging, arising from differences in scanners, acquisition protocols, and patient populations, remains a major challenge for reliable semantic segmentation. Existing unsupervised domain adaptation (UDA) methods predominantly rely on image-level transformations or feature alignment, which often fail to preserve anatomical consistency under large domain shifts. In this work, we propose a novel structured latent UDA framework that performs domain alignment in a topology-aware representation space rather than directly modifying image appearance. Specifically, we introduce a Frequency-Conditioned Graph Diffusion paradigm, where convolutional features are transformed into anatomical graphs to explicitly capture structural relationships. A latent diffusion process then progressively refines these graph embeddings, guided by frequency-aware contextual cues, enabling robust cross-domain alignment. To further enhance generalization, we integrate structural consistency regularization with adversarial latent alignment, eliminating the need for labeled target data. A dedicated decoder reconstructs dense segmentation maps, while stochastic diffusion sampling provides uncertainty estimates for improved potential clinical reliability. Extensive experiments on multiple public medical imaging benchmarks demonstrate that our method consistently outperforms state-of-the-art UDA approaches, achieving superior segmentation accuracy and robustness under significant domain shifts. These results highlight the effectiveness of structured latent modeling and diffusion-based learning for robust domain-adaptive segmentation.

Bridging the Objective Gap: A Unified Pre-Training Framework for Few-Shot Medical Image Segmentation

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency and outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters.