Distribution shift is a common challenge in medical images obtained from different clinical centers, significantly hindering the deployment of pre-trained semantic segmentation models in real-world applications across multiple domains. Continual Test-Time Adaptation (CTTA) has emerged as a promising approach to address cross-domain distribution shifts during continually evolving target domains. Most existing CTTA methods rely on incrementally updating model parameters, which inevitably suffer from error accumulation and catastrophic forgetting, especially in long-term adaptation. Recent prompt-tuning-based works have shown potential to mitigate the two issues above by updating only visual prompts. While these approaches have demonstrated promising performance, several limitations remain: 1) lacking multi-scale prompt diversity, 2) inadequate incorporation of instance-specific knowledge, and 3) risk of privacy leakage. To overcome these limitations, we propose Multi-scale Global-Instance Prompt Tuning (MGIPT), to enhance scale diversity of prompts as well as capture both globaland instance-level knowledge for robust CTTA. Specifically, MGIPT consists of an Adaptive-scale Instance Prompt (AIP) and a Multi-scale Global-level Prompt (MGP). AIP dynamically learns lightweight and instance-specific prompts to mitigate error accumulation with adaptive optimal-scale selection mechanism. MGP captures domain-level knowledge across different scales to ensure robust adaptation with anti-forgetting capabilities. These complementary components are combined through a weighted ensemble approach, enabling effective dual-level adaptation that integrates both global and local information. Extensive experiments on medical image segmentation benchmarks (five optic disc/cup datasets and four polyp datasets) demonstrate that our MGIPT outperforms state-of-the-art methods, achieving robust adaptation across continually changing target domains. Notably, our MGIPT exhibits particularly strong performance in longterm CTTA scenarios, showing great anti-forgetting ability.

Ultrasound videos are an important form of clinical imaging data, and deep learning-based analysis can improve diagnostic accuracy and clinical efficiency. However, the scarcity of labeled data and the inherent challenges of video analysis have impeded the advancement of related methods. In this work, we introduce E-ViM3, a data-efficient Vision Mamba network that preserves the 3D structure of video data, enhancing long-range dependencies and inductive biases to better model spatial-temporal correlations. With our design of Enclosure Global Tokens (EG T), the model captures and aggregates global features more effectively than competing methods. We further employ a tailored masked video modeling approach for self-supervised pre-training to enhance its data efficiency, with the proposed Spatial- Temporal Chained (STC) masking strategy designed to adapt to different video scenarios. Experiments demonstrate that E-ViM3 achieves state-of-the-art performance on different tasks across four datasets of varying sizes: EchoNet-Dynamic, CAMUS, MICCAI-BUV, and WHBUS. Furthermore, our model attains competitive results even with limited labeled data, highlighting its potential impact on real-world clinical applications. Codes are available at https://github.com/HenryZhou19/E-ViM3.

Semantic segmentation is a crucial prerequisite in clinical applications and computer-aided diagnosis. With the development of deep neural networks, biomedical image segmentation has achieved remarkable success. Encoder-Decoder architectures that integrate convolutions and transformers are gaining attention for their potential to capture both global and local features. However, current designs face the contradiction that these two features cannot be continuously transmitted. In addition, some models lack a unified and standardized evaluation benchmark, leading to significant discrepancies in the experimental setup. In this study, we review and summarize these architectures and analyze their contradictions in design. We modify UNet and propose WNet to combine transformers and convolutions, addressing the transmission issue effectively. WNet captures long-range dependencies and local details simultaneously while ensuring their continuous transmission and multi-scale fusion. We integrate WNet into the nnUNet framework for unified benchmarking. Our model achieves state-of-the-art performance in biomedical image segmentation. Extensive experiments demonstrate their effectiveness on four 2D datasets (DRIVE, ISIC-2017, Kvasir-Seg, and CREMI) and four 3D datasets (Parse2022, AMOS22, BTCV, and ImageCAS). The code is available at https://github.com/yanfeng-zhou/nnWNet.

Breast cancer remains a leading cause of death among women, with early detection significantly improving prognosis. Non-contrast computed tomography (NCCT) scans of the chest, routinely acquired for thoracic assessments, often capture the breast region incidentally, presenting an underexplored opportunity for opportunistic breast lesion detection without additional imaging cost or radiation. However, the subtle appearance of lesions in NCCT and the difficulty of jointly modeling lesion detection and malignancy classification pose unique challenges. In this work, we propose Dual-Res Tandem Mamba-3D (DRT-M3D), a novel multitask framework for opportunistic breast cancer analysis on NCCT scans. DRT-M3D introduces a dual-resolution architecture, which captures fine-grained spatial details for segmentation-based lesion detection and global contextual features for breast-level cancer classification. It further incorporates a tandem input mechanism that models bilateral breast regions jointly through Mamba-3D blocks, enabling cross-breast feature interaction by leveraging subtle asymmetries between the two sides. Our approach achieves state-of-the-art performance in both tasks across multi-institutional NCCT datasets spanning four medical centers. Extensive experiments and ablation studies validate the effectiveness of each key component.

MOTIVATION: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets. Additionally, current evaluation metrics are insufficient for effectively capturing segmentation fractures. RESULTS: To address these challenges, we propose a semi-supervised fracture-attention model (SSFA) for tubular object segmentation. SSFA enhances connectivity, reduces fractures, and maintains volumetric accuracy. It outperforms state-of-the-art models in topological performance. Extensive experiments on four public datasets validate the effectiveness of SSFA. Furthermore, we introduce a novel evaluation metric, the fracture rate, which provides an intuitive and quantitative assessment of segmentation fractures. AVAILABILITY AND IMPLEMENTATION: Our source code is available at http://github.com/Yanfeng-Zhou/SSFA.

Semantic segmentation of electron microscopy (EM) images is crucial for nanoscale analysis. With the development of deep neural networks (DNNs), semantic segmentation of EM images has achieved remarkable success. However, current EM image segmentation models are usually extensions or adaptations of natural or biomedical models. They lack the full exploration and utilization of the intrinsic characteristics of EM images. Furthermore, they are often designed only for several specific segmentation objects and lack versatility. In this study, we quantitatively analyze the characteristics of EM images compared with those of natural and other biomedical images via the wavelet transform. To better utilize these characteristics, we design a high-frequency (HF) fusion network, GobletNet, which outperforms state-of-the-art models by a large margin in the semantic segmentation of EM images. We use the wavelet transform to generate HF images as extra inputs and use an extra encoding branch to extract HF information. Furthermore, we introduce a fusion-attention module (FAM) into GobletNet to facilitate better absorption and fusion of information from raw images and HF images. Extensive benchmarking on seven public EM datasets (EPFL, CREMI, SNEMI3D, UroCell, MitoEM, Nanowire and BetaSeg) demonstrates the effectiveness of our model. The code is available at https://github.com/Yanfeng-Zhou/GobletNet.

3D tubular objects have complex spatial shapes. Direct volume visualization cannot intuitively display their morphological characteristics and surface abnormalities. Straightening reformation is an effective visualization method for tubular objects. It uses a swept frame to sample cross sections along the centerline of the tubular object to generate straightening result. So far, however, current methods cannot visualize the full 3D view and fail to interface with downstream morphological analysis. Furthermore, current swept frames impose strict restrictions on the shape of tubular objects and are computationally expensive. In this study, we propose a novel swept frame based on vector rotation and construct an automatic straightening reformation pipeline. Our method is applicable to various tubular objects and can be efficiently executed recursively while ensuring that the straightening results have no rotation bias. Extensive experiments on eight different tubular objects and quantitative evaluation on various downstream applications demonstrate the effectiveness and universality of our straightening pipeline. Code is available at https://github.com/Yanfeng-Zhou/SATO.

XNet introduces a wavelet-based X-shaped unified architecture for fully-and semi-supervised biomedical segmentation. So far, however, XNet still faces the limitations, including performance degradation when images lack high-frequency (HF) information, underutilization of raw images and insufficient fusion. To address these issues, we propose XNet v2, a low-and high-frequency complementary model. XNet v2 performs wavelet-based image-level complementary fusion, using fusion results along with raw images inputs three different sub-networks to construct consistency loss. Furthermore, we introduce a feature-level fusion module to enhance the transfer of low-frequency (LF) information and HF information. XNet v2 achieves state-of-the-art in semi-supervised segmentation while maintaining competitive results in fully-supervised learning. More importantly, XNet v2 excels in scenarios where XNet fails. Compared to XNet, XNet v2 exhibits fewer limitations, better results and greater universality. Extensive experiments on three 2D and two 3D datasets demonstrate the effectiveness of XNet v2. Code is available at https://github.com/Yanfeng-Zhou/XNetv2.

Unsupervised Domain Adaptation (UDA) is a learning technique that transfers knowledge learned in the source domain from labelled training data to the target domain with only unlabelled data. It is of significant importance to medical image segmentation because of the usual lack of labelled training data. Although extensive efforts have been made to optimize UDA techniques to improve the accuracy of segmentation models in the target domain, few studies have addressed the robustness of these models under UDA. In this study, we propose a two-stage training strategy for robust domain adaptation. In the source training stage, we utilize adversarial sample augmentation to enhance the robustness and generalization capability of the source model. And in the target training stage, we propose a novel robust pseudo-label and pseudo-boundary (PLPB) method, which effectively utilizes unlabeled target data to generate pseudo labels and pseudo boundaries that enable model self-adaptation without requiring source data. Extensive experimental results on cross-domain fundus image segmentation confirm the effectiveness and versatility of our method. Source code of this study is openly accessible at https://github.com/LinGrayy/PLPB.

Fully- and semi-supervised semantic segmentation of biomedical images have been advanced with the development of deep neural networks (DNNs). So far, however, DNN models are usually designed to support one of these two learning schemes, unified models that support both fully- and semi-supervised segmentation remain limited. Furthermore, few fully-supervised models focus on the intrinsic low frequency (LF) and high frequency (HF) information of images to improve performance. Perturbations in consistency-based semi-supervised models are often artificially designed. They may introduce negative learning bias that are not beneficial for training. In this study, we propose a wavelet-based LF and HF fusion model XNet, which supports both fully- and semi-supervised semantic segmentation and outperforms state-of-the-art models in both fields. It emphasizes extracting LF and HF information for consistency training to alleviate the learning bias caused by artificial perturbations. Extensive experiments on two 2D and two 3D datasets demonstrate the effectiveness of our model. Code is available at https://github.com/Yanfeng-Zhou/XNet.

MOTIVATION: A protein can be represented in several forms, including its 1D sequence, 3D atom coordinates, and molecular surface. A protein surface contains rich structural and chemical features directly related to the protein's function such as its ability to interact with other molecules. While many methods have been developed for comparing the similarity of proteins using the sequence and structural representations, computational methods based on molecular surface representation are limited. RESULTS: Here, we describe "Surface ID," a geometric deep learning system for high-throughput surface comparison based on geometric and chemical features. Surface ID offers a novel grouping and alignment algorithm useful for clustering proteins by function, visualization, and in silico screening of potential binding partners to a target molecule. Our method demonstrates top performance in surface similarity assessment, indicating great potential for protein functional annotation, a major need in protein engineering and therapeutic design. AVAILABILITY AND IMPLEMENTATION: Source code for the Surface ID model, trained weights, and inference script are available at https://github.com/Sanofi-Public/LMR-SurfaceID.

With the development of the wireless body area network, the demand for medical monitoring is getting higher and higher. A series of wearable devices have now emerged as a means of medical monitoring to detect human bioelectrical signals. This article mainly contains three parts, which are wireless body area network, Electrocardiograph(ECG) signal and Electroencephalogram(EEG) signal acquisition, and the design of wearable acquisition system based on wireless body area network. The first part starts with the performance requirements of the wireless body area network, introduces the performance required for medical monitoring, emphasizes the transmission reliability, and focuses on the key issue of wireless body area network research - energy conservation. The second part introduces the non-contact sensor, and expounds the selection of electrode materials. The design of the acquisition circuit and noise cancellation are mainly discussed. The third part puts forward the structural design of the wearable acquisition system based on wireless body area network, and expounds the data transmission system of the system. Finally, it puts forward suggestions and ideas for the design of body-area network-based wearable EEG and ECG acquisition systems.