Deep learning methods have become a staple in the field of medical image segmentation. While traditional segmentation methods have achieved significant success, challenges such as the labelling of medical image datasets and concerns regarding patient privacy have constrained their practical application. In recent years, incremental learning has garnered considerable attention due to its ability to address these limitations through a step-by-step learning approach. Nevertheless, the inextricable nature of medical images between organisations can result in their catastrophic oblivion. In light of the aforementioned reasons, this study proposes a deep learning framework, CCL, with the objective of systematically addressing the challenge of incremental learning of categories in cardiac magnetic resonance imaging (CMR) segmentation tasks. Rather than employing the reutilisation of pre-existing images, the method deploys the utilisation of pseudo-labels generated from legacy models to facilitate the reutilisation of acquired knowledge. This approach does not involve the reutilisation of preexisting images; rather, it employs pseudo-labels generated by preceding models to reclaim knowledge that has been previously acquired. Firstly, a confidence-based pseudolabel strategy is employed to retrieve old category pixels from the background. Secondly, a contrastive learning mechanism is proposed, based on the characteristics of the teacher-student model. During the training of new models, the teacher model corrects errors of forgetting and consistency caused by incorrect pseudolabels. This ensures maximum retention of prior knowledge without compromising the acquisition of new knowledge. Two publicly available cardiac magnetic resonance datasets were utilised for the purpose of comparison between the proposed methodology and classical approaches. The findings demonstrated that the CCL method outperformed several other excellent methods. The findings of the quantitative analysis suggest that the proposed feature contrast mechanism has a significant effect on the forgetting of old class features, without affecting the learning ability of new classes. The code is available at https://anonymous.4open.science/r/ccl-3C56/.

Deep learning-based cross-modality image translation has demonstrated great potential in supporting clinical diagnosis and medical research. Among existing approaches, CycleGAN has been widely adopted due to its effective unsupervised training mechanism. However, it suffers from two major limitations: (1) its cycle-consistency loss provides only an indirect constraint, often leading to anatomical structure distortions during translation; and (2) its limited capacity to capture global contextual information results in synthetic artifacts in the generated images. To address these issues, we propose a novel and efficient unsupervised framework named ConSSMGAN, which is built upon a cycle-consistent architecture and introduce two key modules: (1) a Contrastive Perception Module, which introduces BoNCE loss based on the contrastive learning to enhance training constraints and alleviate anatomical distortions during translation; and (2) a SSM-Based Global Enhancement Module, which enhances the model's ability to capture global contextual features and fuses them with local representations to reduce synthetic artifacts in the generated results. We validate the effectiveness of our method on both a public pelvic dataset and a private clinical pelvic dataset, comparing it with six state-of-the-art approaches across two key evaluation dimensions: image similarity and anatomical accuracy. Experimental results demonstrate that our method achieves superior performance, highlighting its robustness and potential for clinical application.

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction.

Regularization-based continuous learning (RCL) has been proven to be highly effective on struggling against catastrophic forgetting. However, its application in medical image segmentation (MIS) is relatively scarce. In this paper, we provide a unified description of 6 RCL methods (LwF, LwM, EWC, SI, MAS, and PIGWM) using Taylor expansion and investigate their performances in 2 classic MIS scenarios, namely retinal vessel segmentation (RVS) and cardiac left ventricle segmentation (CLVS) on 8 datasets (CHASE, DRHAGIS, RITE and STARE for the former and M&Ms, LVSC, ACDC and SCD for the latter). We also explore the influence of different task orders (easy to hard or hard to easy), optimizers (Adam or SGD), and parameter capacities (2, 3, 4 or 5 down- and up-sampling pairs) on the performance of these methods. Our experimental results show that these methods are capable of mitigating catastrophic forgetting to a certain extent. Comparing to a hard-to-easy order, most of the methods perform better on all of the already known tasks in an easy-to-hard order. Optimizer Adam performs better on RVS and CLVS. Capacity increases are obviously effective for CLVS, but they have no significant impact on RVS.

Increased pericardial adipose tissue (PEAT) is associated with a series of cardiovascular diseases (CVDs) and metabolic syndromes. Quantitative analysis of PEAT by means of image segmentation is of great significance. Although cardiovascular magnetic resonance (CMR) has been utilized as a routine method for non-invasive and non-radioactive CVD diagnosis, segmentation of PEAT in CMR images is challenging and laborious. In practice, no public CMR datasets are available for validating PEAT automatic segmentation. Therefore, we first release a benchmark CMR dataset, MRPEAT, which consists of cardiac short axis (SA) CMR images from 50 hypertrophic cardiomyopathy (HCM), 50 acute myocardial infarction (AMI), and 50 normal control (NC) subjects. We then propose a deep learning model, named as 3SUnet, to segment PEAT on MRPEAT to tackle the challenges that PEAT is relatively small and diverse and its intensities are hard to distinguish from the background. The 3SUnet is a triple-stage network, of which the backbones are all Unet. One Unet is used to extract a region of interest (ROI) for any given image with ventricles and PEAT being contained completely using a multi-task continual learning strategy. Another Unet is adopted to segment PEAT in ROI-cropped images. The third Unet is utilized to refine PEAT segmentation accuracy guided by an image adaptive probability map. The proposed model is qualitatively and quantitatively compared with the state-of-the-art models on the dataset. We obtain the PEAT segmentation results through 3SUnet, assess the robustness of 3SUnet under different pathological conditions, and identify the imaging indications of PEAT in CVDs. The dataset and all source codes are available at https://dflag-neu.github.io/member/csz/research/.