Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation

Autor: Laura Jeyaseelan, Zhihao Wu, Qian Li, Xiaowei Ding, Jeffrey N. Chiang, Nima Tajbakhsh
Rok vydání: 2019
Předmět:
FOS: Computer and information sciences
Diagnostic Imaging
Computer Science - Machine Learning
Computer science
media_common.quotation_subject
Computer Vision and Pattern Recognition (cs.CV)
Computer Science - Computer Vision and Pattern Recognition
Health Informatics
Machine learning
computer.software_genre
Convolutional neural network
Field (computer science)
030218 nuclear medicine & medical imaging
Machine Learning (cs.LG)
03 medical and health sciences
0302 clinical medicine
Deep Learning
Medical imaging
FOS: Electrical engineering
electronic engineering
information engineering
Humans
Radiology
Nuclear Medicine and imaging
Segmentation
Quality (business)
media_common
Radiological and Ultrasound Technology
business.industry
Deep learning
Image and Video Processing (eess.IV)
Image segmentation
Electrical Engineering and Systems Science - Image and Video Processing
Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Artificial intelligence
Imperfect
Neural Networks
Computer
business
computer
030217 neurology & neurosurgery
Zdroj: Medical image analysis. 63
ISSN: 1361-8423
Popis: The medical imaging literature has witnessed remarkable progress in high-performing segmentation models based on convolutional neural networks. Despite the new performance highs, the recent advanced segmentation models still require large, representative, and high quality annotated datasets. However, rarely do we have a perfect training dataset, particularly in the field of medical imaging, where data and annotations are both expensive to acquire. Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated data is available for training, and weak annotations where the training data has only sparse annotations, noisy annotations, or image-level annotations. In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results. We further compare the benefits and requirements of the surveyed methodologies and provide our recommended solutions. We hope this survey article increases the community awareness of the techniques that are available to handle imperfect medical image segmentation datasets.
Comment: Accepted for publication in the journal of Medical Image Analysis
Databáze: OpenAIRE
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