Imperial College London
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DrWenjiaBai

Faculty of Medicine, Department of Brain Sciences

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 8291w.bai Website

 
 
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Location

 

Room 212, Data Science InstituteWilliam Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Dai:2022:10.1016/j.media.2022.102373,
author = {Dai, C and Wang, S and Mo, Y and Angelini, E and Guo, Y and Bai, W},
doi = {10.1016/j.media.2022.102373},
journal = {Medical Image Analysis},
pages = {1--12},
title = {Suggestive annotation of brain MR images with gradient-guided sampling},
url = {http://dx.doi.org/10.1016/j.media.2022.102373},
volume = {77},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Dai,C
AU  - Wang,S
AU  - Mo,Y
AU  - Angelini,E
AU  - Guo,Y
AU  - Bai,W
DO  - 10.1016/j.media.2022.102373
EP  - 12
PY  - 2022///
SN  - 1361-8415
SP  - 1
TI  - Suggestive annotation of brain MR images with gradient-guided sampling
T2  - Medical Image Analysis
UR  - http://dx.doi.org/10.1016/j.media.2022.102373
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35134636
UR  - https://www.sciencedirect.com/science/article/pii/S1361841522000263?via%3Dihub
UR  - http://hdl.handle.net/10044/1/97187
VL  - 77
ER  -
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