Accurate automatic surgical instrument segmentation plays a crucial role in robot-assisted surgery, but analyzing surgical videos remains challenging due to factors such as rapid instrument movements, high inter-category similarity, and frequent object occlusions. Current surgical instrument segmentation models struggle to capture both inter-frame variations and intra-frame details in complex surgical scenarios. The Segment Anything Model (SAM) has shown significant potential in various segmentation tasks. However, it has not fully addressed the unique challenges posed by surgical videos. To tackle these issues, we propose a Temporal and Distance-Guided SAM model (TD-SAM) for accurate surgical instrument segmentation. Specifically, we introduce a dynamic cross-frame attention module that effectively captures temporal information across frames, allowing the model to track the dynamic changes of surgical instruments and their environment, thus improving segmentation accuracy. In addition, we present a distance-guided instance refinement module, which enhances the model's ability to distinguish between similar categories, mitigating the class ambiguity caused by inter-category similarity. Extensive experiments on the EndoVis18 and EndoVis17 datasets show that the proposed TD-SAM model outperforms existing models, achieving state-of-the-art performance without using any prompts.

Deformable image registration (DIR) is critical for accurate clinical diagnosis and effective treatment planning. However, patient movement, significant intensity differences, and large breathing deformations hinder accurate anatomical alignment in multi-modal image registration. These factors exacerbate the entanglement of anatomical and modality-specific style information, thereby severely limiting the performance of multi-modal registration. To address this, we propose a novel LAtent Diffusion-based Domain-Adaptive feature disentangling (LADDA) framework for unsupervised multi-modal medical image registration, which explicitly addresses the representation disentanglement. First, LADDA extracts reliable anatomical priors from the Latent Diffusion Model (LDM), facilitating downstream content-style disentangled learning. A Domain-Adaptive Feature Disentangling (DAFD) module is proposed to promote anatomical structure alignment further. This module disentangles image features into content and style information, boosting the network to focus on cross-modal content information. Next, a Neighborhood-Preserving Hashing (NPH) is constructed to further perceive and integrate hierarchical content information through local neighbourhood encoding, thereby maintaining cross-modal structural consistency. Furthermore, a Unilateral-Query-Frozen Attention (UQFA) module is proposed to enhance the coupling between upstream prior and downstream content information. The feature interaction within intra-domain consistent structures improves the fine recovery of detailed textures. The proposed framework is extensively evaluated on large-scale multi-center datasets, demonstrating superior performance across diverse clinical scenarios and strong generalization on out-of-distribution (OOD) data.

This paper presents a label-free gland segmentation, GlandSAM, which achieves comparable performance with supervised methods while no label is required during its training or inference phase. We observe that the Segment Anything model produces sub-optimal results on gland dataset: It either over-segments a gland into many fractions or under-segments the gland regions by confusing many of them with the background, due to the complex morphology of glands and lack of sufficient labels. To address this challenge, our GlandSAM innovatively injects two clues about gland morphology into SAM to guide the segmentation process: (1) Heterogeneity within glands and (2) Similarity with the background. Initially, we leverage the clues to decompose the intricate glands by selectively extracting a proposal for each gland sub-region of heterogeneous appearances. Then, we inject the morphology clues into SAM in a fine-tuning manner with a novel morphology-aware semantic grouping module that explicitly groups the high-level semantics of gland sub-regions. In this way, our GlandSAM could capture comprehensive knowledge about gland morphology, and produce well-delineated and complete segmentation results. Extensive experiments conducted on the GlaS dataset and the CRAG dataset reveal that GlandSAM outperforms state-of-the-art label-free methods by a significant margin. Notably, our GlandSAM even surpasses several fully-supervised methods that require pixel-wise labels for training, which highlights the remarkable performance and potential of GlandSAM in the realm of gland segmentation.

Multi-modal information plays a pivotal role in the segmentation of brain tumors. However, previous studies have largely overlooked the distinctive characteristics of individual modalities, which are correlated with the target tumor region due to distinct imaging principles. In this paper, we harness the distinctive traits of individual modalities and introduce a brain tumor segmentation model called specific modality guided brain tumor segmentation model (SMG-BTS). Our SMG-BTS adopts a three-branch encoder-decoder architecture. The main branch utilizes full modalities fused at input-level, while the two affiliated branches operate in parallel to provide guidance to the main branch in acquiring a robust representation. We propose a specific modality spatial guidance (SMSG) module to guide the process of feature extraction. Spatial information is obtained from selected modalities and utilized to enhance features extracted from the main branch. A shared-specific feature enhancement(SSFE) module is proposed to enhance the shared features across modalities and utilizes modality-specific features to further supplement specific information of modalities. Experimental results on the BraTS2021 benchmark dataset demonstrate the effectiveness of our proposed SMG-BTS over state-of-the-art brain tumor segmentation methods.

X-ray images play an important role in the diagnosis of spinal diseases because of their convenient collection and easy observation. But it is time-consuming and challenging for radiologists to examine the differences between the vertebrae to diagnose abnormalities and locate lesions. Many existing methods try to extract the global features of radiographs and do not make full use of adjacent vertebrae variations. In this paper, we propose a novel Axial-aware neural network with Consecutive Attention Transformer (CAT), namely ACAT-Net, which takes advantage of the convolutional neural network and transformer as a new deep learning framework. A deep convolutional network extracts features of anteroposterior and lateral X-ray images that may have abnormalities in them. The consecutive attention transformer block is then used to focus on the morphological differences of axial adjacent vertebrae on the spines. The ingenious structure we designed can significantly reduce the amount of network parameters. Extensive experiments on clinical and public datasets show that our method is remarkably superior to other existing approaches in the spine X-ray image analysis.

Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled images, the network training procedure would suffer from difficulties, leading to a sub-optimal classifier. This problem is even more severe in the medical image analysis field, as the annotation quality of medical images heavily relies on the expertise and experience of annotators. In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data. Specifically, we employ the self-ensemble model with a noisy label filter to efficiently select the clean and noisy samples. Then, the clean samples are trained by a collaborative training strategy to eliminate the disturbance from imperfect labeled samples. Notably, we further design a novel global and local representation learning scheme to implicitly regularize the networks to utilize noisy samples in a self-supervised manner. We evaluated our proposed robust learning strategy on four public medical image classification datasets with three types of label noise, i.e., random noise, computer-generated label noise, and inter-observer variability noise. Our method outperforms other learning from noisy label methods and we also conducted extensive experiments to analyze each component of our method.