Thematische Bibliographien / Histopathological tumor segmentation

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Auswahl der wissenschaftlichen Literatur zum Thema „Histopathological tumor segmentation“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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Zeitschriftenartikel zum Thema "Histopathological tumor segmentation"

Elidrissi, Sofyan, Ikram Ben Abdel Ouahab, Mohammed Bouhorma, and Fatiha Elouaai. "Unveiling the Clinical Significance of Microsatellite Instability in Colorectal Cancer: Deep Learning and the Segment Anything Model for Accurate Segmentation and Classification." International Journal of Online and Biomedical Engineering (iJOE) 21, no. 06 (2025): 97–110. https://doi.org/10.3991/ijoe.v21i06.54491.

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Microsatellite instability (MSI) is crucial for colorectal cancer (CRC) diagnosis and prognosis. Accurate differentiation between MSI and microsatellite stability (MSS) tumors is essential for personalized treatment. This paper introduces a novel approach combining the segment anything model (SAM), Yolov8, and convolutional neural networks (CNNs) for precise segmentation and classification of histopathological images. SAM employs a prompt-based mechanism for segmenting tumor regions like invasive margins, tumor-infiltrating lymphocytes (TILs), and necrotic areas. Integrating SAM’s segmentation

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Liu, Yiqing, Qiming He, Hufei Duan, Huijuan Shi, Anjia Han, and Yonghong He. "Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images." Sensors 22, no. 16 (2022): 6053. http://dx.doi.org/10.3390/s22166053.

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Tumor segmentation is a fundamental task in histopathological image analysis. Creating accurate pixel-wise annotations for such segmentation tasks in a fully-supervised training framework requires significant effort. To reduce the burden of manual annotation, we propose a novel weakly supervised segmentation framework based on sparse patch annotation, i.e., only small portions of patches in an image are labeled as ‘tumor’ or ‘normal’. The framework consists of a patch-wise segmentation model called PSeger, and an innovative semi-supervised algorithm. PSeger has two branches for patch classific

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Zadeh Shirazi, Amin, Eric Fornaciari, Mark D. McDonnell, et al. "The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey." Journal of Personalized Medicine 10, no. 4 (2020): 224. http://dx.doi.org/10.3390/jpm10040224.

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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well a

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van der Kamp, Ananda, Thomas de Bel, Ludo van Alst, et al. "Automated Deep Learning-Based Classification of Wilms Tumor Histopathology." Cancers 15, no. 9 (2023): 2656. http://dx.doi.org/10.3390/cancers15092656.

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(1) Background: Histopathological assessment of Wilms tumors (WT) is crucial for risk group classification to guide postoperative stratification in chemotherapy pre-treated WT cases. However, due to the heterogeneous nature of the tumor, significant interobserver variation between pathologists in WT diagnosis has been observed, potentially leading to misclassification and suboptimal treatment. We investigated whether artificial intelligence (AI) can contribute to accurate and reproducible histopathological assessment of WT through recognition of individual histopathological tumor components. (

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Park, Youngjae, Jinhee Park, and Gil-Jin Jang. "Efficient Perineural Invasion Detection of Histopathological Images Using U-Net." Electronics 11, no. 10 (2022): 1649. http://dx.doi.org/10.3390/electronics11101649.

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Perineural invasion (PNI), a sign of poor diagnosis and tumor metastasis, is common in a variety of malignant tumors. The infiltrating patterns and morphologies of tumors vary by organ and histological diversity, making PNI detection difficult in biopsy, which must be performed manually by pathologists. As the diameters of PNI nerves are measured on a millimeter scale, the PNI region is extremely small compared to the whole pathological image. In this study, an efficient deep learning-based method is proposed for detecting PNI regions in multiple types of cancers using only PNI annotations wit

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Sun, Yibao, Zhaoyang Xu, Yihao Guo, et al. "Scale-Adaptive viable tumor burden estimation via histopathological microscopy image segmentation." Computers in Biology and Medicine 189 (May 2025): 109915. https://doi.org/10.1016/j.compbiomed.2025.109915.

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Mutkule, Prasad R., Nilesh P. Sable, Parikshit N. Mahalle, and Gitanjali R. Shinde. "Histopathological parameter and brain tumor mapping using distributed optimizer tuned explainable AI classifier." Journal of Autonomous Intelligence 7, no. 5 (2024): 1617. http://dx.doi.org/10.32629/jai.v7i5.1617.

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<p>Brain tumors represent a critical and severe challenge worldwide early and accurate diagnosis is necessary to increase the predictions for individuals with brain tumors. Several studies on brain tumor mapping have been conducted recently; however, the methods have some drawbacks, including poor image quality, a lack of data, and a limited capacity for generalization ability. To tackle these drawbacks this research presents a distributed optimizer tuned explainable AI classifier model for brain tumor mapping from histopathological images. The foraging gyps africanus optimization enable

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Althubaity, DaifAllah D., Faisal Fahad Alotaibi, Abdalla Mohamed Ahmed Osman, et al. "Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis." Journal of Personalized Medicine 13, no. 3 (2023): 388. http://dx.doi.org/10.3390/jpm13030388.

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Background: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. Objective: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. Method: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA

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Altini, Nicola, Emilia Puro, Maria Giovanna Taccogna, et al. "Tumor Cellularity Assessment of Breast Histopathological Slides via Instance Segmentation and Pathomic Features Explainability." Bioengineering 10, no. 4 (2023): 396. http://dx.doi.org/10.3390/bioengineering10040396.

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The segmentation and classification of cell nuclei are pivotal steps in the pipelines for the analysis of bioimages. Deep learning (DL) approaches are leading the digital pathology field in the context of nuclei detection and classification. Nevertheless, the features that are exploited by DL models to make their predictions are difficult to interpret, hindering the deployment of such methods in clinical practice. On the other hand, pathomic features can be linked to an easier description of the characteristics exploited by the classifiers for making the final predictions. Thus, in this work,

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Wu, Rujuan, Jiayi Yang, Qi Chen, et al. "Distinguishing of Histopathological Staging Features of H-E Stained Human cSCC by Microscopical Multispectral Imaging." Biosensors 14, no. 10 (2024): 467. http://dx.doi.org/10.3390/bios14100467.

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Cutaneous squamous cell carcinoma (cSCC) is the second most common malignant skin tumor. Early and precise diagnosis of tumor staging is crucial for long-term outcomes. While pathological diagnosis has traditionally served as the gold standard, the assessment of differentiation levels heavily depends on subjective judgments. Therefore, how to improve the diagnosis accuracy and objectivity of pathologists has become an urgent problem to be solved. We used multispectral imaging (MSI) to enhance tumor classification. The hematoxylin and eosin (H&E) stained cSCC slides were from Shanghai Ruiji

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Dissertationen zum Thema "Histopathological tumor segmentation"

Lerousseau, Marvin. "Weakly Supervised Segmentation and Context-Aware Classification in Computational Pathology." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG015.

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L’anatomopathologie est la discipline médicale responsable du diagnostic et de la caractérisation des maladies par inspection macroscopique, microscopique, moléculaire et immunologique des tissus. Les technologies modernes permettent de numériser des lames tissulaire en images numériques qui peuvent être traitées par l’intelligence artificielle pour démultiplier les capacités des pathologistes. Cette thèse a présenté plusieurs approches nouvelles et puissantes qui s’attaquent à la segmentation et à la classification pan-cancer des images de lames numériques. L’apprentissage de modèles de segme

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Huang, Pei-Chen, and 黃珮楨. "Real Time Automatic Lung Tumor Segmentation in Whole-slide Histopathological Images." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2h8u6r.

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Buchteile zum Thema "Histopathological tumor segmentation"

Lerousseau, Marvin, Maria Vakalopoulou, Marion Classe, et al. "Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59722-1_45.

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Hieber, Daniel, Nico Haisch, Gregor Grambow, et al. "Comparing nnU-Net and deepflash2 for Histopathological Tumor Segmentation." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240487.

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Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring year

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Spiess, Ellena, Dominik Müller, Moritz Dinser, et al. "Automatic Segmentation of Histopathological Glioblastoma Whole-Slide Images Utilizing MONAI." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250279.

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Manual segmentation of histopathological images is both resource-intensive and prone to human error, particularly when dealing with challenging tumor types like Glioblastoma (GBM), an aggressive and highly heterogeneous brain tumor. The fuzzy borders of GBM make it especially difficult to segment, requiring models with strong generalization capabilities to achieve reliable results. In this study, we leverage the Medical Open Network for Artificial Intelligence (MONAI) framework to segment GBM tissue from hematoxylin and eosin-stained Whole-Slide Images. MONAI performed comparably well to state

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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

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Konferenzberichte zum Thema "Histopathological tumor segmentation"

Mezgebo, Biniyam, Joema Lima, Abdoljalil Addeh, et al. "Attention-Enhanced UNet for Automated Gleason Score 3 Tumor Segmentation in Histopathological Whole Slide Images." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980899.

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Huang, Xiansong, Hongliang He, Pengxu Wei, Chi Zhang, Juncen Zhang, and Jie Chen. "Tumor Tissue Segmentation for Histopathological Images." In MMAsia '19: ACM Multimedia Asia. ACM, 2019. http://dx.doi.org/10.1145/3338533.3372210.

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Musulin, Jelena, Daniel Štifanić, Ana Zulijani, and Zlatan Car. "SEMANTIC SEGMENTATION OF ORAL SQUAMOUS CELL CARCINOMA ON EPITHELLIAL AND STROMAL TISSUE." In 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.194m.

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Oral cancer (OC) is among the top ten cancers worlwide, with more than 90% being squamous cell carcinoma. Despite diagnostic and therapeutic development in OC patients’ mortality and morbidity rates remain high with no advancement in the last 50 years. Development of diagnostic tools in identifying pre-cancer lesions and detecting early-stage OC might contribute to minimal invasive treatment/surgery therapy, improving prognosis and survival rates, and maintaining a high quality of life of patients. For this reason, Artificial Intelligence (AI) algorithms are widely used as a computational aid

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