A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images

Wang, Xiyue; Zhang, Jun; Yang, Sen; Xiang, Jingxi; Luo, Feng; Wang, Minghui; Zhang, Jing; Yang, Wei; Huang, Junzhou; Han, Xiao

doi:10.1016/j.media.2022.102703

Cited by 22 publications

(14 citation statements)

References 36 publications

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“…It is also possible that our normalisation method was not sufficient to prevent a domain shift in the external test dataset. Other method such as Fourier-based data augmentation as described in Wang et al 24 could be adopted in a follow-up study to overcome this issue. We have chosen to train a U-Net model as this has been shown to be one of the most often-used models for automatic delineation in histopathology.…”

Section: Discussionmentioning

confidence: 99%

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Beuque

Magee

Chatterjee

et al. 2023

Journal of Pathology Informatics

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Section: Discussionmentioning

confidence: 99%

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Beuque

Magee

Chatterjee

et al. 2023

Journal of Pathology Informatics

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“…As an emerging next-generation segmentation model emphasizing generalizability and prompt functionality, SAM demonstrates such potential but its performance on medical images remains unclear, especially for radiotherapy. 43 Current research on SAM in the field of medical imaging primarily follows two directions: Firstly, evaluating the performance of SAM across various types of medical images [44][45][46][47][48][49] ; and secondly, enhancing its performance through improvements to specific modules of SAM. [50][51][52][53][54][55] These studies, to some extent, demonstrated the generalizability of SAM.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the data used originated from public datasets, leading to a lack of performance assessment in realworld clinical settings. [44][45][46][47][48][49][50][51][52][53][54] Research reports on the application of SAM in auto-segmentation for clinical radiation therapy are still limited. Florian et al reported on the performance of SAM in the auto-segmentation of brain tumors in MRI images, but no further evaluation for the generalizability and human-AI interaction was reported, which is potentially the most important feature as the next generation auto-segmentation model for clinical radiation therapy.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology

Zhang,

Liu,

Zhang

et al. 2024

Medical Physics

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BackgroundEfficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning‐based auto‐segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning‐based auto‐segmentation approaches face two challenges in clinical practice: generalizability and human‐AI interaction. A generalizable and promptable auto‐segmentation model, which segments OARs of multiple disease sites simultaneously and supports on‐the‐fly human‐AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning.PurposeMeta's segment anything model (SAM) was proposed as a generalizable and promptable model for next‐generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation.MethodsComputed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's ’segment anything’ mode, and automatic segmentation using SAM's ‘box prompt’ mode that implements manual interaction via live prompts during segmentation.ResultsOur results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea.ConclusionsOur results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box‐prompt method enables the users to augment auto‐segmentation interactively and dynamically, leading to patient‐specific auto‐segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto‐segmentation model in radiotherapy.

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“… 10 , 11 , 12 However, due to the diversity of mitotic nuclei shapes and textures, customizing these hand-crafted feature-based methods for mitosis detection tasks is difficult, and the outcomes are often unsatisfactory. 13 …”

Section: Introductionmentioning

confidence: 99%

Toward interpretable and generalized mitosis detection in digital pathology using deep learning

Farooq,

Saleem,

Aleem

et al. 2024

DIGITAL HEALTH

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Objective The mitotic activity index is an important prognostic factor in the diagnosis of cancer. The task of mitosis detection is difficult as the nuclei are microscopic in size and partially labeled, and there are many more non-mitotic nuclei compared to mitotic ones. In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method to tackle these challenges. Methods Our proposed methodology is inspired from recent research on deep learning and an extensive analysis on the dataset and training pipeline. We first used the MiDoG′22 dataset for training, validation, and testing. We then tested the methodology without fine-tuning on the TUPAC′16 dataset and on a real-time case from Shaukat Khanum Memorial Cancer Hospital and Research Centre. Results Our methodology has shown promising results both quantitatively and qualitatively. Quantitatively, our methodology achieved an F1-score of 0.87 on the MiDoG'22 dataset and an F1-score of 0.83 on the TUPAC dataset. Qualitatively, our methodology is generalizable and interpretable across various datasets and clinical settings. Conclusion In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method that can accurately predict mitotic nuclei. We illustrate the accuracy, generalizability, and interpretability of our approach across various datasets and clinical settings. Our methodology can speed up the adoption of computer-aided digital pathology in clinical settings.

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A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images

Cited by 22 publications

References 36 publications

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Automated detection and delineation of lymph nodes in haematoxylin & eosin stained digitised slides

Technical note: Generalizable and promptable artificial intelligence model to augment clinical delineation in radiation oncology

Toward interpretable and generalized mitosis detection in digital pathology using deep learning

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