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MSNSegNet: attention-based multi-shape nuclei instance segmentation in histopathology images

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第6期62卷 1821-1836页

作者： Qian, Ziniu Wang, Zihua Zhang, Xin Wei, Bingzheng Lai, Maode Shou, Jianzhong Fan, Yubo Xu, Yan Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Xiaomi Corp Beijing 100085 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Dis Prote Sch Med Dept Pathol Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Alibaba Zhejiang Univ Joint Res Ctr Future Digital Hangzhou 310027 Zhejiang Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Canc Ctr Natl Clin Res Ctr Canc Dept UrolCanc Hosp Beijing 100021 Peoples R China

In clinical research, the segmentation of irregularly shaped nuclei, particularly in mesenchymal areas like fibroblasts, is crucial yet often neglected. These irregular nuclei are significant for assessing tissue repair in immunotherapy, a process involving neovascularization and fibroblast proliferation. Proper segmentation of these nuclei is vital for evaluating immunotherapy's efficacy, as it provides insights into pathological features. However, the challenge lies in the pronounced curvature variations of these non-convex nuclei, making their segmentation more difficult than that of regular nuclei. In this work, we introduce an undefined task to segment nuclei with both regular and irregular morphology, namely multi-shape nuclei segmentation. We propose a proposal-based method to perform multi-shape nuclei segmentation. By leveraging the two-stage structure of the proposal-based method, a powerful refinement module with high computational costs can be selectively deployed only in local regions, improving segmentation accuracy without compromising computational efficiency. We introduce a novel self-attention module to refine features in proposals for the sake of effectiveness and efficiency in the second stage. The self-attention module improves segmentation performance by capturing long-range dependencies to assist in distinguishing the foreground from the background. In this process, similar features get high attention weights while dissimilar ones get low attention weights. In the first stage, we introduce a residual attention module and a semantic-aware module to accurately predict candidate proposals. The two modules capture more interpretable features and introduce additional supervision through semantic-aware loss. In addition, we construct a dataset with a proportion of non-convex nuclei compared with existing nuclei datasets, namely the multi-shape nuclei (MsN) dataset. Our MSNSegNet method demonstrates notable improvements across various metrics compare

关键词： nuclei instance segmentation Self-attention Proposal-based method Semantic-aware

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nuclei probability and centroid map network for nuclei instance segmentation in histology images

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NEURAL COMPUTING & APPLICATIONS 2023年第21期35卷 15447-15460页

作者： Rashid, Syed Nauyan Fraz, Muhammad Moazam Natl Univ Sci & Technol NUST Islamabad 44000 Pakistan

nuclei instance segmentation is an integral step in digital pathology workflow as it is a prerequisite for most downstream tasks such as patient survival analysis, precision medicine, and cancer prognosis. There exist many challenges such as quality of labeled data, staining variation among tissue slides, high variation among multi-organ & multi-center digital slides and overlapping nuclei that are difficult to separate. Therefore, it is important to have an automatic and robust nuclei instance segmentation model that saves the time of pathologists by delineating accurate nuclei instances. To this end, we develop a nuclei instance segmentation pipeline that estimates distance transform and nuclear masks using an encoder-decoder-based CNN model. These estimated distance transform and nuclear masks are then utilized to delineate accurate nuclei boundaries from overlapping nuclei. We demonstrate that our proposed NC-Net model is lightweight and produces state-of-the-art results on the three recently published largest nuclei instance segmentation datasets to date. Additionally, our proposed NC-Net model is faster and utilizes a fewer number of parameters for learning as compared to other top-performing nuclei instance segmentation models. The purpose of developing a lightweight and state-of-the-art model is to provide capacity building to digital pathology workflows by reducing inference times and delineating accurate nuclear instances. The implementation details and the trained models are made available at this https://***/nauyan/NC-Net.

关键词： Nuclear segmentation nuclei instance segmentation Computational pathology Whole slide images Deep learning

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A Fast and Accurate Algorithm for nuclei instance segmentation in Microscopy Images

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IEEE ACCESS 2020年 8卷 158679-158689页

作者： Cheng, Zhiming Qu, Aiping Univ South China Sch Comp Hengyang 421001 Peoples R China Hunan Med Big Data Int Sci & Technol Innovat Coop Hengyang 421001 Peoples R China

nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed. However, this task is still challenging because of the large variability among different types of nuclei. Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources. In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method. Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes. Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei. The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed. The source code will be released at: https://***/QUAPNH/nucleiseg.

关键词： Image segmentation Microscopy Task analysis Feature extraction Computational modeling Encoding Semantics nuclei instance segmentation microscopy images convolutional neural network

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HDA-Net: H&E and RGB Dual Attention Network for nuclei instance segmentation

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IEEE ACCESS 2024年 12卷 56622-56632页

作者： Im, Yu-Han Park, Seo-Hyeong Lee, Sang-Chul Inha Univ Dept Comp Sci & Engn Incheon 22211 South Korea DeepCardio Co Ltd Incheon 21984 South Korea

H&E-stained images (HSIs) are widely adopted for revealing cellular structures and capturing morphological changes in nuclear instance segmentation. Despite several studies aimed at differentiating pixels of overlapping nuclei, challenges persist due to color inconsistencies introduced by non-uniform manual staining operations. This issue leads to blurred borders and alterations in color representation within the images. This study proposes an H&E and RGB dual attention network (HDA-Net) designed to address these challenges and enhance nuclei instance segmentation accuracy. Specifically, our approach involves decomposing hematoxylin, eosin, and residual (HER) components from HSIs using color deconvolution to extract discriminative information, such as nuclei and cytoplasm, entangled in RGB images. Additionally, we propose an H&E and RGB dual attention module (HDA) and a single-source attention module (SAM) to effectively incorporate information from the decomposed HER to enhance RGB images. The HDA module utilizes cross-attention between RGB features and decomposed HER to enhance RGB representation under the guidance of the HER components. Conversely, SAM applies global channel attention to learn the correlations between channels of each RGB and HER component. In the final decoder stage, the study employs multi-task learning to capture region and centroid information of the label, providing comprehensive supervision for clustered and overlapped nuclei. Our experiments on the CoNSeP, PanNuke, and Kumar datasets demonstrated that the proposed HDA-Net outperformed existing models, showing improvements of 1.2% in AJI and 0.76% in Dice for CoNSeP, 0.7% and 0.5% for PanNuke, and 0.21% and 0.16% for Kumar, respectively.

关键词： nuclei instance segmentation attention mechanism guided feature attention

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nuclei instance segmentation using a transformer-based graph convolutional network and contextual information augmentation

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 167卷 107622-107622页

作者： Wang, Juan Zhang, Zetao Wu, Minghu Ye, Yonggang Wang, Sheng Cao, Ye Yang, Hao Hubei Univ Technol Sch Elect & Elect Engn Wuhan Hubei Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan Peoples R China

Nucleus instance segmentation is an important task in medical image analysis involving cell-level pathological analysis and is of great significance for many biomedical applications, such as disease diagnosis and drug screening. However, the high-density and tight-contact between cells is a common feature of most cell images, which poses a great technical challenge for nuclei instance segmentation. The latest research focuses on CNNbased methods for nuclei instance segmentation, which typically rely on bounding box regression and nonmaximum suppression to locate nuclei. However, this frequently results in poor local bounding boxes for nuclei that are adhered or clustered together. In response to the challenges of high-density and tight-contact in cellular images, we propose a novel end-to-end nuclei instance segmentation model. Specifically, we first employ the Swin Transformer as the backbone network of our model, which captures global multi-scale information by combining the global modelling capability of transformers and the local modelling capability of convolutional neural networks (CNNs). Additionally, we integrate a graph convolutional feature fusion module (GCFM), that combines deep and shallow features to learn an affinity matrix. The module also adopts graph convolution to guide the network in learning the object-level local information. Finally, we design a hybrid dilated convolution module (HDC) and insert it into the backbone network to enhance the contextual information over a large range. These components assist the network in extracting rich features. The experimental results demonstrate that our algorithm outperforms several state-of-the-art models on the DSB2018 and LIVECell datasets.

关键词： nuclei instance segmentation Microscopic pathological images Swin transformer Graph convolution

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Disentanglement-based Unsupervised Domain Adaptation for nuclei instance segmentation 39

Disentanglement-based Unsupervised Domain Adaptation for Nuc...

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39th International Conference on Image and Vision Computing New Zealand

作者： Long, Jieting Liu, Dongnan Tang, Zihao Cai, Weidong Univ Sydney Sch Comp Sci Sydney NSW Australia Univ Sydney Brain & Mind Ctr Sydney NSW Australia

ISBN: (纸本)9798331518783;9798331518776

Unsupervised domain adaptation (UDA) for nuclei instance segmentation is vital for pathology cell analysis. Though deep learning-based models have achieved promising performance on the nuclei instance segmentation task, they require rich and reliable annotations which are generally not available in digital pathology studies. UDA techniques can therefore address the domain difference among different modalities and limited annotation problems by transferring knowledge among domains. One key limitation of conventional UDA methods is the entanglement of domain-specific factors with the learned features during adaptation. To tackle this problem, we propose a UDA framework from the perspective of feature disentanglement for instance segmentation in microscopy images. More specifically, we design a novel multi-modal reconstruction (MMR) module to separate domain-invariant and domain-specific features with the least missing information via a feature reconstruction technique. Experimental results on two UDA instance segmentation tasks demonstrate that our proposed method outperforms the state-of-the-art UDA methods.

关键词： Unsupervised Domain Adaptation nuclei instance segmentation Feature Disentanglement

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Learning Erosional Probability Maps for nuclei instance segmentation 3

Learning Erosional Probability Maps for Nuclei Instance Segm...

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IEEE 3rd International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR)

作者： Huang, Zhongyi Ding, Yao Geng, Ruizhe He, Hongliang Huang, Xiansong Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China Peng Cheng Lab Res Ctr Artificial Intelligence Shenzhen Peoples R China

ISBN: (纸本)9781728142722

nuclei instance segmentation is an essential but challenging topic in computer vision and medical image analysis due to the ambiguity of nuclei boundaries, the severe border adhesions, and the diversity of nuclei shapes and sizes. In this paper, we propose a simple yet effective proposal-free framework for accurate nuclei instance segmentation. Specifically, we adopt a deep regression model to learn the mapping from the input images to the nuclei erosional inside probability maps and obtain the final instance segmentation results through straightforward dilation post-processing. We evaluate our framework on the open available 2018 Data Science Bowl nuclei instance segmentation dataset and outperform the state-of-the-art methods.

关键词： nuclei instance segmentation erosional probability map deep learning cell boundary ambiguity

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An Integration Convolutional Neural Network for nuclei instance segmentation

An Integration Convolutional Neural Network for Nuclei Insta...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Qu, Aiping Cheng, Zhiming He, Xiaofeng Li, Yue Univ South China Sch Comp Hengyang Peoples R China

ISBN: (纸本)9781728162157

Automated detection and segmentation of every nucleus in microscopy images is a fundamental task in biomedical research and clinical practice, including nuclear morphology analysis, cancer grading and prognosis predicting. However, this task is still challenging because of the nuclear atypical in shape, size and internal organisation. In this paper, we propose a nuclei instance segmentation method with the aim of jointing detection and segmentation simultaneously. The method builds on a two-stream Convolutional Neural Network(CNN) architecture that explicit utilize a single shot multi-box detector with Feature Pyramid Network in one stream for obtaining the nuclear location information and keep an U-net in the other stream for nuclei instance segmentation. Furthermore, the recurrent, residual and attention mechanisms are integrated for focusing on the useful information. The approach utilizes the strengths of the Recurrent Convolutional Neural Network (RCNN), the Attention Network and the Residual Network. The experiments demonstrate that the proposed nuclei instance segmentation approach outperforms prior state-of-the-art methods, and could be generalized across variety of nuclear type, magnification and imaging modality.

关键词： nuclei instance segmentation Convolutional neural network Microscopy image

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nuclei instance segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset nuclei instance segmentation nuclei segmentation YOLOv7

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Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part2期79卷 000-000页

作者： Dogar, G. Murtaza Shahzad, Muhammad Fraz, Muhammad Moazam Natl Univ Sci & Technol NUST Islamabad Pakistan Tech Univ Munich D-80333 Munich Germany

nuclei instance segmentation and classification in histology plays a major role in routine pathology image examination, which enable morphological features analysis that further facilitates streamlined diagnosis and prognosis quantification. However, the nuclei in the tissue images obtained from different human organs are characterized with high variability in shape, size, morphology and spatial arrangements. Moreover, during digitization of tissue slide, the image quality is degraded because of added artifacts, poor contrast, blurred regions due to failed auto-focus and inconsistent staining procedure. Owing to these challenges, it is difficult to build a generalized feature representation that can achieve precise segmentation and classification of nuclei instances in complex tumor micro-environment of tissue specimens obtained from various organs. To address these problems, we propose a novel deep learning model, that harnesses horizontal and vertical distance information hidden among the nuclei instances to successfully delineate the challenging nuclei. Our proposed methodology uses soft attention mechanism to generate relevant feature activation and prune irrelevant and noisy information. These attention units produce more precise and refined feature maps resulting in finer instances segmentation and accurate classification in the overlapping nuclei, the nuclei with touching boundaries and reduction in false positives. We train our model on publicly available data-sets (Kumar, CoNSep, CPM-17 and a new data-set PanNuke). Our methodology shows superior performance in nuclei classification and segmentation in comparison with recently published methods. The code and the obtained results have been made public at the following link: http://***/eGkV.

关键词： Histopathology Digital pathology Cancer image analytics nuclei classification nuclei instance segmentation

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