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3D Image segmentation with sparse annotation by self-training and internal registration
, Article IEEE Journal of Biomedical and Health Informatics ; 2020 ; Nikdan, M ; Soleymanibaghshah, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...
3D Image Segmentation with Sparse Annotation by Self-Training and Internal Registration
, Article IEEE Journal of Biomedical and Health Informatics ; Volume 25, Issue 7 , 2021 , Pages 2665-2672 ; 21682194 (ISSN) ; Nikdan, M ; Baghshah, M. S ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...