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Suggestive annotation of brain MR images with gradient-guided sampling

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Title: Suggestive annotation of brain MR images with gradient-guided sampling
Authors: Dai, C
Wang, S
Mo, Y
Angelini, E
Guo, Y
Bai, W
Item Type: Journal Article
Abstract: Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of machine learning, in particular supervised learning, depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire, it takes a substantial amount of time and resource to curate an annotated medical image set. In this paper, we propose an efficient annotation framework for brain MR images that can suggest informative sample images for human experts to annotate. We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation. Experiments show that for brain tumour segmentation task on the BraTS 2019 dataset, training a segmentation model with only 7% suggestively annotated image samples can achieve a performance comparable to that of training on the full dataset. For whole brain segmentation on the MALC dataset, training with 42% suggestively annotated image samples can achieve a comparable performance to training on the full dataset. The proposed framework demonstrates a promising way to save manual annotation cost and improve data efficiency in medical imaging applications.
Issue Date: 24-Jan-2022
Date of Acceptance: 18-Jan-2022
URI: http://hdl.handle.net/10044/1/97187
DOI: 10.1016/j.media.2022.102373
ISSN: 1361-8415
Publisher: Elsevier
Start Page: 1
End Page: 12
Journal / Book Title: Medical Image Analysis
Volume: 77
Copyright Statement: © 2022 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: National Institutes of Health
Funder's Grant Number: NIHR
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Brain MRI
Suggestive annotation
Image segmentation
Active learning
Active learning
Brain MRI
Image segmentation
Suggestive annotation
Brain
Brain Neoplasms
Diagnostic Imaging
Humans
Image Processing, Computer-Assisted
Machine Learning
Magnetic Resonance Imaging
Brain
Humans
Brain Neoplasms
Diagnostic Imaging
Magnetic Resonance Imaging
Image Processing, Computer-Assisted
Machine Learning
Active learning
Brain MRI
Image segmentation
Suggestive annotation
09 Engineering
11 Medical and Health Sciences
Nuclear Medicine & Medical Imaging
Publication Status: Published
Conference Place: Netherlands
Online Publication Date: 2022-01-24
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Computing
Faculty of Medicine
Department of Brain Sciences