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Suggestive annotation of brain MR images with gradient-guided sampling
- Source :
- Medical Image Analysis. 77:102373
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
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.<br />Comment: Manuscript accepted by MedIA
- Subjects :
- FOS: Computer and information sciences
Diagnostic Imaging
Technology
Active learning
Computer Science - Artificial Intelligence
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Health Informatics
Computer Science, Artificial Intelligence
09 Engineering
Machine Learning
Engineering
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Engineering, Biomedical
11 Medical and Health Sciences
Image segmentation
Science & Technology
Radiological and Ultrasound Technology
Brain Neoplasms
Radiology, Nuclear Medicine & Medical Imaging
Brain
Magnetic Resonance Imaging
Computer Graphics and Computer-Aided Design
Nuclear Medicine & Medical Imaging
Artificial Intelligence (cs.AI)
Brain MRI
Computer Science
Suggestive annotation
Computer Science, Interdisciplinary Applications
Computer Vision and Pattern Recognition
Life Sciences & Biomedicine
Subjects
Details
- ISSN :
- 13618415
- Volume :
- 77
- Database :
- OpenAIRE
- Journal :
- Medical Image Analysis
- Accession number :
- edsair.doi.dedup.....1dcd97c79c10a0289dc6efebb17f484a