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Jensen MP, Qiang Z, Khan DZ, Stoyanov D, Baldeweg SE, Jaunmuktane Z, Brandner S, Marcus HJ. Artificial intelligence in histopathological image analysis of central nervous system tumours: A systematic review. Neuropathol Appl Neurobiol 2024; 50:e12981. [PMID: 38738494 DOI: 10.1111/nan.12981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/05/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024]
Abstract
The convergence of digital pathology and artificial intelligence could assist histopathology image analysis by providing tools for rapid, automated morphological analysis. This systematic review explores the use of artificial intelligence for histopathological image analysis of digitised central nervous system (CNS) tumour slides. Comprehensive searches were conducted across EMBASE, Medline and the Cochrane Library up to June 2023 using relevant keywords. Sixty-eight suitable studies were identified and qualitatively analysed. The risk of bias was evaluated using the Prediction model Risk of Bias Assessment Tool (PROBAST) criteria. All the studies were retrospective and preclinical. Gliomas were the most frequently analysed tumour type. The majority of studies used convolutional neural networks or support vector machines, and the most common goal of the model was for tumour classification and/or grading from haematoxylin and eosin-stained slides. The majority of studies were conducted when legacy World Health Organisation (WHO) classifications were in place, which at the time relied predominantly on histological (morphological) features but have since been superseded by molecular advances. Overall, there was a high risk of bias in all studies analysed. Persistent issues included inadequate transparency in reporting the number of patients and/or images within the model development and testing cohorts, absence of external validation, and insufficient recognition of batch effects in multi-institutional datasets. Based on these findings, we outline practical recommendations for future work including a framework for clinical implementation, in particular, better informing the artificial intelligence community of the needs of the neuropathologist.
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Affiliation(s)
- Melanie P Jensen
- Pathology Department, Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
- Briscoe Lab, The Francis Crick Institute, London, UK
| | - Zekai Qiang
- School of Medicine and Population Health, University of Sheffield Medical School, Sheffield, UK
| | - Danyal Z Khan
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Computer Science, University College London, London, UK
| | - Danail Stoyanov
- Department of Computer Science, University College London, London, UK
| | - Stephanie E Baldeweg
- Department of Diabetes and Endocrinology, University College London Hospitals, London, UK
- Centre for Obesity and Metabolism, Department of Experimental and Translational Medicine, Division of Medicine, University College London, London, UK
| | - Zane Jaunmuktane
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
| | - Sebastian Brandner
- Division of Neuropathology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- Department of Computer Science, University College London, London, UK
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Chelebian E, Avenel C, Ciompi F, Wählby C. DEPICTER: Deep representation clustering for histology annotation. Comput Biol Med 2024; 170:108026. [PMID: 38308865 DOI: 10.1016/j.compbiomed.2024.108026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/24/2024] [Accepted: 01/24/2024] [Indexed: 02/05/2024]
Abstract
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully supervised segmentation requires large-scale data manually annotated by experts, which can be expensive and time-consuming to obtain. Non-fully supervised methods, ranging from semi-supervised to unsupervised, have been proposed to address this issue and have been successful in WSI segmentation tasks. But these methods have mainly been focused on technical advancements in algorithmic performance rather than on the development of practical tools that could be used by pathologists or researchers in real-world scenarios. In contrast, we present DEPICTER (Deep rEPresentatIon ClusTERing), an interactive segmentation tool for histopathology annotation that produces a patch-wise dense segmentation map at WSI level. The interactive nature of DEPICTER leverages self- and semi-supervised learning approaches to allow the user to participate in the segmentation producing reliable results while reducing the workload. DEPICTER consists of three steps: first, a pretrained model is used to compute embeddings from image patches. Next, the user selects a number of benign and cancerous patches from the multi-resolution image. Finally, guided by the deep representations, label propagation is achieved using our novel seeded iterative clustering method or by directly interacting with the embedding space via feature space gating. We report both real-time interaction results with three pathologists and evaluate the performance on three public cancer classification dataset benchmarks through simulations. The code and demos of DEPICTER are publicly available at https://github.com/eduardchelebian/depicter.
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Affiliation(s)
- Eduard Chelebian
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden.
| | - Chirstophe Avenel
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden
| | - Francesco Ciompi
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Carolina Wählby
- Department of Information Technology and SciLifeLab, Uppsala University, Uppsala, Sweden
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Drioua WR, Benamrane N, Sais L. Breast Cancer Histopathological Images Segmentation Using Deep Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:7318. [PMID: 37687772 PMCID: PMC10490494 DOI: 10.3390/s23177318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/10/2023] [Accepted: 08/18/2023] [Indexed: 09/10/2023]
Abstract
Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which remains a costly and difficult task. Thus, the use of an unsupervised segmentation method could facilitate the process. In this article, we propose two approaches for the semantic segmentation of breast cancer histopathology images. On the one hand, an autoencoder architecture for unsupervised segmentation is proposed, and on the other hand, an improvement U-Net architecture for supervised segmentation is proposed. We evaluate these models on a public dataset of histological images of breast cancer. In addition, the performance of our segmentation methods is measured using several evaluation metrics such as accuracy, recall, precision and F1 score. The results are competitive with those of other modern methods.
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Affiliation(s)
- Wafaa Rajaa Drioua
- Laboratoire SIMPA, Département d’Informatique, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB), Oran 31000, Algeria;
| | - Nacéra Benamrane
- Laboratoire SIMPA, Département d’Informatique, Université des Sciences et de la Technologie d’Oran Mohamed Boudiaf (USTO-MB), Oran 31000, Algeria;
| | - Lakhdar Sais
- Centre de Recherche en Informatique de Lens, CRIL, CNRS, Université d’Artois, 62307 Lens, France;
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Ali NA, El Abbassi A, Bouattane O. Performance evaluation of spatial fuzzy C-means clustering algorithm on GPU for image segmentation. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:6787-6805. [PMID: 35968411 PMCID: PMC9363269 DOI: 10.1007/s11042-022-13635-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/25/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Image processing by segmentation technique is an important phase in medical imaging such as MRI. Its objective is to analyze the different tissues in human body. In research area, Fuzzy set is one of the most successful techniques that guarantees a robust classification. Spatial FCM (SFCM); one of the fuzzy c-means variants; considers spatial information to deal with the noisy images. To reduce this iterative algorithm's execution time, a hard SIMD architecture has been planted named the Graphical Processing Unit (GPU). In this work, a great contribution has been done to diagnose, confront and implement three different parallel implementations on GPU. A parallel implementations' extensive study of SFCM entitled PSFCM using 3 × 3 window is presented, and the experiments illustrate a significant decrease in terms of running time of this algorithm known by its high complexity. The experimental results indicate that the parallel version's execution time is about 9.46 times faster than the sequential implementation on image segmentation. This gain in terms of speed-up is achieved on the Nvidia GeForce GT 740 m GPU.
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Affiliation(s)
- Noureddine Ait Ali
- Labo ERTTI, FST Errachidia, Moulay Ismail University of Meknes, Meknes, Morocco
| | - Ahmed El Abbassi
- Labo ERTTI, FST Errachidia, Moulay Ismail University of Meknes, Meknes, Morocco
| | - Omar Bouattane
- SSDIA Laboratory, ENSET-Mohammedia Hassan II University Casablanca, Casablanca, Morocco
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Fu R, Li Z. An evidence accumulation based block diagonal cluster model for intent recognition from EEG. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103835] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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