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A neural architecture search based framework for segmentation of epithelium, nuclei and oral epithelial dysplasia grading
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Azarmehr, N., Shephard, Adam, Mahmood, H., Rajpoot, Nasir M. (Nasir Mahmood) and Khurram, S. A. (2022) A neural architecture search based framework for segmentation of epithelium, nuclei and oral epithelial dysplasia grading. In: Yang, G. and Aviles-Rivero, A. and Roberts, M. and Schönlieb, C. B., (eds.) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, 13413 . Cham: Springer New York LLC, pp. 357-370. ISBN 9783031120527
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Official URL: https://doi.org/10.1007/978-3-031-12053-4_27
Abstract
Oral epithelial dysplasia (OED) is a pre-cancerous histopathological diagnosis given to a range of oral lesions. Architectural, cytological and histological features of OED can be modelled through the segmentation of full epithelium, individual nuclei and stroma (connective tissues) to provide significant diagnostic features. In this paper, we explore a customised neural architecture search (NAS) based method for optimisation of an efficient architecture for segmentation of the full epithelium and individual nuclei in pathology whole slide images (WSIs). Our initial experimental results show that the NAS-derived architecture achieves 93.5% F1-score for the full epithelium segmentation and 94.5% for nuclear segmentation outperforming other state-of-the-art models. Accurate nuclear segmentation allows us to perform quantitative statistical and morphometric feature analyses of the segmented nuclei within regions of interest (ROIs) of multi-gigapixel whole-slide images (WSIs). We show that a random forest model using these features can differentiate between low-risk and high-risk OED lesions.
| Item Type: | Book Item | ||||
|---|---|---|---|---|---|
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||
| Series Name: | Lecture Notes in Computer Science | ||||
| Publisher: | Springer New York LLC | ||||
| Place of Publication: | Cham | ||||
| ISBN: | 9783031120527 | ||||
| Book Title: | Medical Image Understanding and Analysis. MIUA 2022 | ||||
| Editor: | Yang, G. and Aviles-Rivero, A. and Roberts, M. and Schönlieb, C. B. | ||||
| Official Date: | 25 July 2022 | ||||
| Dates: |
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| Volume: | 13413 | ||||
| Page Range: | pp. 357-370 | ||||
| DOI: | 10.1007/978-3-031-12053-4_27 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Copyright Holders: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG |
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