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Suggestive annotation of brain MR images with gradient-guided sampling
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![]() | Accepted version | 2.55 MB | Adobe PDF | View/Open |
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 |