Melanoma segmentation using deep learning with test-time augmentations and conditional random fields

In a computer-aided diagnostic (CAD) system for skin lesion segmentation, variations in shape and size of the skin lesion makes the segmentation task more challenging. Lesion segmentation is an initial step in CAD schemes as it leads to low error rates in quantification of the structure, boundary, a...

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Vydáno v:Scientific reports Ročník 12; číslo 1; s. 3948 - 16
Hlavní autoři: Ashraf, Hassan, Waris, Asim, Ghafoor, Muhammad Fazeel, Gilani, Syed Omer, Niazi, Imran Khan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 10.03.2022
Nature Publishing Group
Nature Research
Nature Portfolio
Témata:
692/699
692/700
Automation
Datasets
Deep Learning
Dermoscopy
Dermoscopy - methods
Engineering, computing & technology
Humanities and Social Sciences
Humans
Ingénierie, informatique & technologie
Lesions
Melanoma
Melanoma - diagnostic imaging
Multidisciplinary
Neural Networks, Computer
Science
Science (multidisciplinary)
Segmentation
Skin Diseases
Skin lesions
Skin Neoplasms
Skin Neoplasms - diagnostic imaging
Skin Neoplasms - pathology
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:In a computer-aided diagnostic (CAD) system for skin lesion segmentation, variations in shape and size of the skin lesion makes the segmentation task more challenging. Lesion segmentation is an initial step in CAD schemes as it leads to low error rates in quantification of the structure, boundary, and scale of the skin lesion. Subjective clinical assessment of the skin lesion segmentation results provided by current state-of-the-art deep learning segmentation techniques does not offer the required results as per the inter-observer agreement of expert dermatologists. This study proposes a novel deep learning-based, fully automated approach to skin lesion segmentation, including sophisticated pre and postprocessing approaches. We use three deep learning models, including UNet, deep residual U-Net (ResUNet), and improved ResUNet (ResUNet++). The preprocessing phase combines morphological filters with an inpainting algorithm to eliminate unnecessary hair structures from the dermoscopic images. Finally, we used test time augmentation (TTA) and conditional random field (CRF) in the postprocessing stage to improve segmentation accuracy. The proposed method was trained and evaluated on ISIC-2016 and ISIC-2017 skin lesion datasets. It achieved an average Jaccard Index of 85.96% and 80.05% for ISIC-2016 and ISIC-2017 datasets, when trained individually. When trained on combined dataset (ISIC-2016 and ISIC-2017), the proposed method achieved an average Jaccard Index of 80.73% and 90.02% on ISIC-2017 and ISIC-2016 testing datasets. The proposed methodological framework can be used to design a fully automated computer-aided skin lesion diagnostic system due to its high scalability and robustness.
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scopus-id:2-s2.0-85126244339
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-07885-y