2021
DOI: 10.1093/neuonc/noab196.568
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Nimg-71. Identifying Clinically Applicable Machine Learning Algorithms for Glioma Segmentation Using a Systematic Literature Review

Abstract: PURPOSE Nowadays Machine learning (ML) algorithms are often used for segmentation of gliomas, but which algorithms provide the most accurate method for implementation into clinical practice has not fully been identified. We performed a systematic review of the literature to characterize the methods used for glioma segmentation and their accuracy. METHODS In accordance to PRISMA, a literature review was performed on four datab… Show more

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“…Semi-automated segmentations generate automated ROIs that need to be checked and modified by experts. Fully automatic segmentations, on the other hand, are DL-generated (most frequently by convolutional neural networks (CNNs)), which automatically delineate ROIs and omit the need for manual labor [ 33 ]. In general, semi-automated segmentations are considered to be more reliable and transparent than fully automatic segmentations.…”
Section: Workflow For Developing Prediction Modelsmentioning
confidence: 99%
“…Semi-automated segmentations generate automated ROIs that need to be checked and modified by experts. Fully automatic segmentations, on the other hand, are DL-generated (most frequently by convolutional neural networks (CNNs)), which automatically delineate ROIs and omit the need for manual labor [ 33 ]. In general, semi-automated segmentations are considered to be more reliable and transparent than fully automatic segmentations.…”
Section: Workflow For Developing Prediction Modelsmentioning
confidence: 99%