Using image-level weakly supervised semantic segmentation (WSSS) techniques to segment tissue regions in giga-pixel histopathological whole slide images (WSI) has garnered widespread attention, as it can reduce many annotation workloads for pathologists. Most recent studies are based on class activation mapping (CAM) to generate pseudo masks, which are then used to train segmentation model in a fully supervised manner. However, it is still a challenge to accurately segment non-predominant tissue categories due to the existence of long-tailed and inter-class homogeneity matters. For these matters, we propose three designs to solve them: 1) Diffusion-based Data Generation to synthesis new images of tail class to expand data distribution; 2) Feature Recalibration to reassign the logits in CAM to narrow the feature-level prediction gap between predominant and non-predominant classes; 3) Grade-skip Learning to correct the under-fitting tendency of hard samples during the segmentation phase. Moreover, we also design a powerful pipeline LoHo for histopathology tissue segmentation. Extensive experiments demonstrate that our method not only achieves new state-of-the-art performances but also significantly improves segmentation of tail classes. In addition, our methods are plug-and-play, making it easily integrable into many mainstream WSSS frameworks.

Image-level weakly supervised semantic segmentation (WSSS) reduces the dependence on high-quality data annotation, which plays a crucial role in computational pathology. Benefit from the ability to localize the objects with only binary labels, Class Activation Map (CAM) is a widely used method to initial pseudo masks. However, due to the low contrast among different tissues in histopathological images, most existing CAM-based methods perform poorly in gland segmentation. We retrospect this process and find that class consistency and semantic consistency can guide the network to effectively distinguish confusing pixels and generate fine-grained pseudo masks. Specifically, for class consistency, we propose Consistency Correlation Attention (CCA) to encourage the network to focus on the contribution of class features to semantic dependencies. For semantic consistency, we propose Multi-scale Pyramid Fusion Pooling (MPFP) to aggregate coarse-to-fine global semantic information from CAMs at multiple spatial resolutions, thus identifying class localization. Additionally, we introduce a Purified Labels Filtration (PLF) strategy during the segmentation phase to mitigate the noisy supervision signal and improve the segmentation quality of the model. Extensive experiments show that the our method achieves new state-of-the-art results on three publicly available gland datasets. Furthermore, our method demonstrates impressive domain adaptation capability, achieving satisfactory results with only a small portion of samples when faced with unseen domain data.

Mitosis counting is crucial for breast cancer grading and prognosis but is traditionally manual, time-consuming, and subjective. Existing methods focus on patch-level features and neglect important contextual and pathological information. In this paper, we propose a prior knowledge-assisted feature enhancement classification framework that first leverages nucleuslevel features to enhance image-level detection performance. Specifically, a nuclear graph is constructed with nuclei as nodes, where node features are designed based on prior knowledge of nuclear structural irregularities, density, cytoplasm properties, and staining characteristics. The Adjacent nuclei background is added as edge features to enhance contextual relationships. A graph neural network is applied to iteratively propagate node information and integrate edge features, with a Sequential Feature Learning (SFL) module further enhancing the dynamically updated nucleus representations into refined nucleus-level features. An inter-modal mutual learning (IML) is introduced to enable complementary learning between nucleus-level features and image-level features from a convolutional neural network. Experiments on MIDOG2021, MIDOG2022, and GZMH-V2 datasets show superior performance with weak supervision using only point labels.

In the field of gland segmentation in histopathology, deep-learning methods have made significant progress. However, most existing methods not only require a large amount of high-quality annotated data but also tend to confuse the internal of the gland with the background. To address this challenge, we propose a new semi-supervised method named DCCL-Seg for gland segmentation, which follows the teacher-student framework. Our approach can be divided into follows steps. First, we design a contrastive learning module to improve the ability of the student model's feature extractor to distinguish between gland and background features. Then, we introduce a Signed Distance Field (SDF) prediction task and employ dual-consistency strategy (across tasks and models) to better reinforce the learning of gland internal. Next, we proposed a pseudo label filtering and reweighting mechanism, which filters and reweights the pseudo labels generated by the teacher model based on confidence. However, even after reweighting, the pseudo labels may still be influenced by unreliable pixels. Finally, we further designed an assistant predictor to learn the reweighted pseudo labels, which do not interfere with the student model's predictor and ensure the reliability of the student model's predictions. Experimental results on the publicly available GlaS and CRAG datasets demonstrate that our method outperforms other semi-supervised medical image segmentation methods.

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods.

The topology integrity of blood vessel segmentation is crucial for clinical disease diagnosis. However, existing methods for enhancing vessel topology have overlooked the degree and type of topology interaction, and have not incorporated topology contrast relation into consideration. In this letter, we propose confindence-based topolopy interaction and topology contrast loss method to enhance the interaction and contrast relations for intra-class and inter-class of topology structure. Additionally, considering the influence of multiscale feature on topology learning, we propose lightweight global feature extraction and align fusion to better capture global features and mitigate feature misalignment. The quantitative and qualitative comparisons on the DRIVE and STARE datasets show the superiority of the proposed model. Furthermore, the improvements experiments of the advanced topology enhancement networks using our proposed methods, and ablation experiments on DCA1 datasets provide evidence of the effectiveness of the proposed method.

Deep learning-based vessel segmentation tasks serve as important auxiliary tools for disease diagnosis. However, the region connecting the foreground and background, which is named the edge-buffer region in this letter, suffers from noisy labels and a lack of discriminative features due to low contrast and limitations of imaging devices. To address these limitations, we propose a self-correcting region supervision to revamp the noisy labels in the edge-buffer region. Furthermore, we introduce the concept of treating the edge-buffer region independently from the foreground and background, leveraging the designed contrastive learning method and edge-blur-guided module to enhance discriminative learning ability and collaborative learning ability across different regions, respectively. The experimental results comparison with other classical and state-of-the-art methods on DRIVE, CHASEDB1, and DCA1 datasets has proven the effectiveness of the proposed methods.

Ray-casting algorithm is an important volume rendering algorithm, which is widely used in medical image processing. Aiming to address the shortcomings of the current ray-casting algorithms in 3D reconstruction of medical images, such as slow rendering speed and low sampling efficiency, an improved algorithm based on dynamic adaptive sampling is proposed. By using the central difference gradient method, the corresponding sampling interval is obtained dynamically according to the different sampling points. Meanwhile, a new rendering operator is proposed based on the color value and opacity changes before and after the ray enters the volume element, and the resistance luminosity. Compared with the state of other algorithms, experimental results show that the method proposed in this paper has a faster rendering speed while ensuring the quality of the generated image.