Relevant bibliographies by topics / Multi-stain segmentation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multi-stain segmentation'

Author: Grafiati
Published: 23 November 2024
Last updated: 19 July 2025

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Journal articles on the topic "Multi-stain segmentation"

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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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Dissertations / Theses on the topic "Multi-stain segmentation"

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Nisar, Zeeshan. "Self-supervised learning in the presence of limited labelled data for digital histopathology." Electronic Thesis or Diss., Strasbourg, 2024. http://www.theses.fr/2024STRAD016.

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Un défi majeur dans l'application de l'apprentissage profond à l'histopathologie réside dans la variation des colorations, à la fois inter et intra-coloration. Les modèles d'apprentissage profond entraînés sur une seule coloration (ou domaine) échouent souvent sur d'autres, même pour la même tâche (par exemple, la segmentation des glomérules rénaux). L'annotation de chaque coloration est coûteuse et chronophage, ce qui pousse les chercheurs à explorer des méthodes de transfert de coloration basées sur l'adaptation de domaine. Celles-ci visent à réaliser une segmentation multi-coloration en uti
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Book chapters on the topic "Multi-stain segmentation"

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Taixé, L. Leal, A. U. Coskun, B. Rosenhahn, and D. H. Brooks. "Automatic Segmentation of Arteries in Multi-stain Histology Images." In IFMBE Proceedings. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_531.

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Kim, Ho Heon, Won Chan Jeong, Youngjin Park, and Young Sin Ko. "Understanding Stain Separation Improves Cross-Scanner Adenocarcinoma Segmentation with Joint Multi-Task Learning." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250272.

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Digital pathology has made significant advances in tumor diagnosis and segmentation; however, image variability resulting from tissue preparation and digitization - referred to as domain shift - remains a significant challenge. Variations caused by heterogeneous scanners introduce color inconsistencies that negatively affect the performance of segmentation algorithms. To address this issue, we have developed a joint multitask U-net architecture trained for both segmentation and stain separation. This model isolates the stain matrix and stain density, allowing it to handle color variations and improve generalization across different scanners. On 180 stain images from three different scanners, our model achieved a Dice score of 0.898 and an Intersection Over Union (IoU) score of 0.816, outperforming conventional supervised learning methods by +1.5% and +2.5%, respectively. On external datasets containing images from six different scanners, our model averaged a Dice score and IoU of 0.792. By leveraging our novel approach to stain separation, we improved segmentation accuracy and generalization across diverse histopathological samples. These advances may pave the way for more reliable and consistent diagnostic tools for breast adenocarcinoma.
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Conference papers on the topic "Multi-stain segmentation"

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Wang, Ruochan, and Sei-ichiro Kamata. "Stain-Refinement and Boundary-Enhancement Weight Maps for Multi-organ Nuclei Segmentation." In 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2020. http://dx.doi.org/10.1109/icievicivpr48672.2020.9306586.

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Wang, Ruochan, and Sei-ichiro Kamata. "Stain-Refinement and Boundary-Enhancement Weight Maps for Multi-organ Nuclei Segmentation." In 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR). IEEE, 2020. http://dx.doi.org/10.1109/icievicivpr48672.2020.9306586.

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Graham, S., and N. M. Rajpoot. "SAMS-NET: Stain-aware multi-scale network for instance-based nuclei segmentation in histology images." In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE, 2018. http://dx.doi.org/10.1109/isbi.2018.8363645.

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