Adaptive Mix for Semi-Supervised Medical Image Segmentation

Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules, such as replacing low-confidence patches with high-confidence ones. The former lacks control over the perturbation degree, leading to overfitting on randomly perturbed samples, while the latter tends to generate images with trivial perturbations, both of which limit the effectiveness of consistency learning. This paper aims to answer the following question: How can image mix-up perturbation be adaptively performed during training? To this end, we propose an Adaptive Mix algorithm (AdaMix) for image mix-up in a self-paced learning manner. Given that, in general, a model's performance gradually improves during training, AdaMix is equipped with a self-paced curriculum that, in the initial training stage, provides relatively simple perturbed samples and then gradually increases the difficulty of perturbed images by adaptively controlling the perturbation degree based on the model's learning state estimated by a self-paced regularize. We develop three frameworks with our AdaMix, i.e., AdaMix-ST, AdaMix-MT, and AdaMix-CT, for semi-supervised medical image segmentation. Extensive experiments on three public datasets, including both 2D and 3D modalities, show that the proposed frameworks are capable of achieving superior performance. For example, compared with the state-of-the-art, AdaMix-CT achieves relative improvements of 2.62% in Dice and 48.25% in average surface distance on the ACDC dataset with 10% labeled data. The results demonstrate that mix-up operations with dynamically adjusted perturbation strength based on the segmentation model's state can significantly enhance the effectiveness of consistency regularization.

Self-Paced Sample Selection for Barely-Supervised Medical Image Segmentation

The existing barely-supervised medical image segmentation (BSS) methods, adopting a registration-segmentation paradigm, aim to learn from data with very few annotations to mitigate the extreme label scarcity problem. However, this paradigm poses a challenge: pseudo-labels generated by image registration come with significant noise. To address this issue, we propose a self-paced sample selection framework (SPSS) for BSS. Specifically, SPSS comprises two main components: 1) self-paced uncertainty sample selection (SU) for explicitly improving the quality of pseudo labels in the image space, and 2) self-paced bidirectional feature contrastive learning (SC) for implicitly improving the quality of pseudo labels through enhancing the separability between class semantics in the feature space. Both SU and SC are trained collaboratively in a self-paced learning manner, ensuring that SPSS can learn from high-quality pseudo labels for BSS. Extensive experiments on two public medical image segmentation datasets demonstrate the effectiveness and superiority of SPSS over the state-of-the-art. Our code is release at https://github.com/SuuuJM/SPSS.

Rethinking Barely-Supervised Segmentation from an Unsupervised Domain Adaptation Perspective

This paper investigates an extremely challenging problem, barely-supervised medical image segmentation (BSS), where the training dataset comprises limited labeled data with only single-slice annotations and numerous unlabeled images. Currently, state-of-the-art (SOTA) BSS methods utilize a registration-based paradigm, depending on image registration to propagate single-slice annotations into volumetric pseudo labels for constructing a complete labeled set. However, this paradigm has a critical limitation: the pseudo labels generated by image registration are unreliable and noisy. Motivated by this, we propose a new perspective: training a model using only single-annotated slices as the labeled set without relying on image registration. To this end, we formulate BSS as an unsupervised domain adaptation (UDA) problem. Specifically, we first design a novel noise-free labeled data construction algorithm (NFC) for slice-to-volume labeled data synthesis, which may result in a side effect: domain shifts between the synthesized images and the original images. Then, a frequency and spatial mix-up strategy (FSX) is further introduced to mitigate the domain shifts for UDA. Extensive experiments demonstrate that our method provides a promising alternative for BSS. Remarkably, the proposed method with only one labeled slice achieves an 80.77% dice score on left atrial segmentation, outperforming the SOTA by 61.28%. The code will be released upon the publication of this paper.