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Journal articles on the topic 'Multi-stain segmentation'

Author: Grafiati

Published: 23 November 2024

Last updated: 31 July 2025

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1

Hassan, Loay, Mohamed Abdel-Nasser, Adel Saleh, Osama A. Omer, and Domenec Puig. "Efficient Stain-Aware Nuclei Segmentation Deep Learning Framework for Multi-Center Histopathological Images." Electronics 10, no. 8 (2021): 954. http://dx.doi.org/10.3390/electronics10080954.

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Existing nuclei segmentation methods have obtained limited results with multi-center and multi-organ whole-slide images (WSIs) due to the use of different stains, scanners, overlapping, clumped nuclei, and the ambiguous boundary between adjacent cell nuclei. In an attempt to address these problems, we propose an efficient stain-aware nuclei segmentation method based on deep learning for multi-center WSIs. Unlike all related works that exploit a single-stain template from the dataset to normalize WSIs, we propose an efficient algorithm to select a set of stain templates based on stain clusterin

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Abdel-Nasser, Mohamed, Vivek Kumar Singh, and Ehab Mahmoud Mohamed. "Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network." Diagnostics 12, no. 12 (2022): 3024. http://dx.doi.org/10.3390/diagnostics12123024.

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Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose

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Cruz, Yanna Leidy Ketley Fernandes, Antonio Fhillipi Maciel Silva, Ewaldo Eder Carvalho Santana, and Daniel G. Costa. "Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review." Applied Sciences 15, no. 14 (2025): 7802. https://doi.org/10.3390/app15147802.

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Histological image analysis plays a crucial role in understanding and diagnosing various diseases, but manually segmenting these images is often complex, time-consuming, and heavily reliant on expert knowledge. Generative adversarial networks (GANs) have emerged as promising tools to assist in this task, enhancing the accuracy and efficiency of segmentation in histological images. This systematic literature review aims to explore how GANs have been utilized for segmentation in this field, highlighting the latest trends, key challenges, and opportunities for future research. The review was cond

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Blagojević, Nikola, Igor Mihajlović, Jovana Džunić та ін. "Abstract LB348: ProMiSЕ: Probabilistic multi-stain estimator for color separation in multiplexed brightfield histopathology images". Cancer Research 85, № 8_Supplement_2 (2025): LB348. https://doi.org/10.1158/1538-7445.am2025-lb348.

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Abstract Introduction: Brightfield histopathology uses chromogenic staining to assign distinct colors to different specimen elements. Multiplexing biomarker staining on the same slide has valuable advantages, including the ability to capture the spatial distribution of different cell populations and tissue structures. Color separation is a computational method that separates contributions from each chromogen. It enables the analysis of individual stain expressions, and serves as a crucial preprocessing step in numerous histopathology image analysis tasks (e.g. nuclear segmentation, stain norma

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Xu, Xiaoping, Meirong Ji, Bobin Chen, and Guowei Lin. "Analysis on Characteristics of Dysplasia in 345 Patients with Myelodysplastic Syndrome." Blood 112, no. 11 (2008): 5100. http://dx.doi.org/10.1182/blood.v112.11.5100.5100.

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Abstract Objective To investigate the characteristics of dysplasia in myelodysplastic syndrome (MDS). Methods Collect 716 samples of adult patients with abnormal blood routine but unclear cause between July 04, 2003 and March 14, 2007. Based on the gold standard of WHO MDS classification, all cases were detected on cytomorphological observation, cytochemical stain, bone marrow pathological study, cytogenetics, flow cytometry, and ect. The bone marrow cytological study on some abnormal hematopoietic cells has a diagnostic value to determine clonal or non-clonal diseases and assess sensitivity a

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Baird, Regan, Fabian Schneider, Edward Lo, Tad George, and Joshua Nordberg. "Abstract 6482: Phenoplex™ spatial analysis of whole-slide colon adenocarcinoma imaged with Orion™ 13-plex one-round staining and imaging." Cancer Research 85, no. 8_Supplement_1 (2025): 6482. https://doi.org/10.1158/1538-7445.am2025-6482.

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Background: The ability to stain and image tissue at high-plex over an entire slide in a single round enables insights into tissue architecture and the molecular mechanisms of immune and disease processes at unprecedented throughput. Here we investigate a whole slide tissue section of high-grade colon adenocarcinoma, a hot tumor that contains prominent clusters of PD-L1 expression scattered throughout, using single-step high-plex staining and imaging at single-cell resolution followed by the analysis of single-cell phenotypes, tissue segmentation and spatial proximity and nearest neighbor anal

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Alvarsson, Alexandra, Carl Storey, Brandy Olin Pope, et al. "Abstract 6624: 3D assessment of the lung cancer microenvironment using multi-resolution open-top light-sheet microscopy." Cancer Research 83, no. 7_Supplement (2023): 6624. http://dx.doi.org/10.1158/1538-7445.am2023-6624.

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Abstract Background: Non-small cell lung cancer (NSCLC) tissue is a valuable resource for diagnosis, treatment planning, and drug development. Current 2D histopathological techniques introduce under-sampling error (i.e., a single 5 um section represents 0.5% of a 1 mm thick biopsy), interobserver variability, and fail to capture the biology contained within the entire tissue sample. We have developed a suite of technologies to stain, chemically clarify, image, visualize, and analyze entire intact NSCLC tissue samples. Methods: Human NSCLC tissue, stored frozen in OCT, was fixed in 4% paraforma

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Schuerch, Christian, Graham L. Barlow, Salil S. Bhate, Nikolay Samusik, Garry P. Nolan, and Yury Goltsev. "Dynamics of the Bone Marrow Microenvironment during Leukemic Progression Revealed By Codex Hyper-Parameter Tissue Imaging." Blood 132, Supplement 1 (2018): 935. http://dx.doi.org/10.1182/blood-2018-99-111708.

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Abstract Introduction The bone marrow (BM) microenvironment consists of various cell types such as mesenchymal stromal cells, endothelial cells, osteoblastic cells and multiple immune cell types including mature myeloid cells and lymphocytes. Recent studies have shown that leukemias can create and maintain a leukemia-supporting BM microenvironment, and vice versa, a dysfunctional BM microenvironment can contribute to leukemia development and progression. Moreover, in tumors the microenvironment is often immunosuppressive and restrains effective anti-tumoral immune responses by adaptive and inn

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Wang, Chong, Yajie Wan, Shuxin Li, et al. "SegAnyPath: A Foundation Model for Multi-resolution Stain-variant and Multi-task Pathology Image Segmentation." IEEE Transactions on Medical Imaging, 2024, 1. http://dx.doi.org/10.1109/tmi.2024.3501352.

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Warr, Ryan, Stephan Handschuh, Martin Glösmann, Robert J. Cernik, and Philip J. Withers. "Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-23592-0.

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AbstractChemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of mul

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Gour, Mahesh, Sweta Jain, and T. Sunil Kumar. "Robust nuclei segmentation with encoder‐decoder network from the histopathological images." International Journal of Imaging Systems and Technology 34, no. 4 (2024). http://dx.doi.org/10.1002/ima.23111.

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AbstractNuclei segmentation is a prerequisite and an essential step in cancer detection and prognosis. Automatic nuclei segmentation from the histopathological images is challenging due to nuclear overlap, disease types, chromatic stain variability, and cytoplasmic morphology differences. Furthermore, it is demanding to develop a single accurate method for segmenting nuclei of different organs because of the diversity in nuclei size, shape, and appearance across the various organs. To address these challenges, we developed a robust Encoder‐Decoder network for nuclei segmentation from the multi

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Huang, Zhi, Wei Shao, Zhi Han, et al. "Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images." npj Precision Oncology 7, no. 1 (2023). http://dx.doi.org/10.1038/s41698-023-00352-5.

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AbstractAdvances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex I

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Cazzaniga, Giorgio, Mattia Rossi, Albino Eccher, et al. "Time for a full digital approach in nephropathology: a systematic review of current artificial intelligence applications and future directions." Journal of Nephrology, September 28, 2023. http://dx.doi.org/10.1007/s40620-023-01775-w.

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Abstract Introduction Artificial intelligence (AI) integration in nephropathology has been growing rapidly in recent years, facing several challenges including the wide range of histological techniques used, the low occurrence of certain diseases, and the need for data sharing. This narrative review retraces the history of AI in nephropathology and provides insights into potential future developments. Methods Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about automated image analysis or the application of an AI algorithm on

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Li, Shaoli, and Wang Liu. "Research on partition method of bearing circumferential surface based on machine vision." 计算机科学辑要, May 9, 2025, 135. https://doi.org/10.63313/cs.8013.

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As a key component of industrial machinery, the surface defects of bearings will directly af-fect the safety of industrial production, so it is of great significance to carry out quality inspec-tion of bearings in actual industrial production. Because the defects in different positions on the cir-cumferential surface have different damage degrees to the bearing, the defects in the core area of the circumferential surface are the most harmful. Therefore, based on machine vision inspec-tion, which is the mainstream apparent quality inspection technology of industrial prod-ucts. The essence of th

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15

Warr, Ryan, Evelina Ametova, Robert J. Cernik, et al. "Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction." Scientific Reports 11, no. 1 (2021). http://dx.doi.org/10.1038/s41598-021-00146-4.

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AbstractHere we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (~ 1 keV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achie

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Sundharbaabu, Priyannth Ramasami, Junhyuck Chang, Yunchul Kim, et al. "Artificial Intelligence‐Enhanced Analysis of Genomic DNA Visualized with Nanoparticle‐Tagged Peptides under Electron Microscopy." Small, October 9, 2024. http://dx.doi.org/10.1002/smll.202405065.

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AbstractDNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for electron microscopy (EM), notwithstanding its ability to furnish a broad magnification range and high‐resolution details for observing DNA molecules. Herein, a non‐toxic, universal, and simple method is proposed that uses gold nanoparticle‐tagged peptides to stain all types of naturally occurring DNA molecules, enabling their visualization under EM. This method enhances the current DNA visualization capabilities, allowing for sequence‐spec

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Zhao, Junhan, Xiyue Wang, Junyou Zhu, et al. "PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI." Light: Science & Applications 12, no. 1 (2023). http://dx.doi.org/10.1038/s41377-023-01296-y.

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AbstractOrganoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual flu

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18

Zeng, Ziwei, Huashan Liu, Zuofu Peng, Yun Jia, Wenpan Zhang, and Liang Kang. "Development and validation of a multi-feature immune scoring prognostic model for stage II and III rectal cancer patients." Journal of Clinical Oncology 43, no. 16_suppl (2025). https://doi.org/10.1200/jco.2025.43.16_suppl.e15636.

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e15636 Background: Stage II and III rectal cancer patients undergoing direct surgery after clinical diagnosis remain at risk of postoperative recurrence, with significant variability in their prognoses. Immune scoring, based on the assessment of immune cells within the tumor microenvironment, offers a promising approach to predict outcomes and guide clinical decision-making. Methods: Multiplex immunohistochemistry (mIHC) was performed to simultaneously stain CD3, CD8, and PANCK on a single slide. Besides, an algorithm was developed for image segmentation, region identification, and cell recogn

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Mereuta, Oana Madalina, Seán Fitzgerald, Trace A. Christensen, et al. "High-resolution scanning electron microscopy for the analysis of three-dimensional ultrastructure of clots in acute ischemic stroke." Journal of NeuroInterventional Surgery, December 23, 2020, neurintsurg—2020–016709. http://dx.doi.org/10.1136/neurintsurg-2020-016709.

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BackgroundCharacterization of acute ischemic stroke (AIS) clots has typically focused on two-dimensional histological analysis of the thrombus. The three-dimensional (3D) architecture and distribution of components within emboli have not been fully investigated. The aim of this study was to examine the composition and microstructure of AIS clots using histology and serial block-face scanning electron microscopy (SBFSEM).MethodsAs part of the multi-institutional STRIP registry, 10 consecutive AIS emboli were collected from 10 patients treated by mechanical thrombectomy. Histological and immunoh

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