CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation

• URL: http://arxiv.org/abs/2205.09601v1
• Date: Thu, 19 May 2022 14:42:49 GMT
• Title: CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation
• Authors: Can Taylan Sari, Sila Kurugol, Onur Afacan, Simon K. Warfield
• Abstract summary: We propose a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation. The experimental results demonstrate the superiority of the proposed framework over the baseline method both qualitatively and quantitatively.
• Score: 3.1657395760137406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation of brain magnetic resonance images (MRI) is crucial for the analysis of the human brain and diagnosis of various brain disorders. The drawbacks of time-consuming and error-prone manual delineation procedures are aimed to be alleviated by atlas-based and supervised machine learning methods where the former methods are computationally intense and the latter methods lack a sufficiently large number of labeled data. With this motivation, we propose CORPS, a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation. In this work, we propose to generate expert-level pseudo-labels for unlabeled set of images in an order based on a local intensity-based similarity score to existing labeled set of images and using a novel atlas-based label fusion method. Then, we propose to train a 3D DCNN on the combination of expert and pseudo labeled images for binary segmentation of each anatomical structure. The binary segmentation approach is proposed to avoid the poor performance of multi-class segmentation methods on limited and imbalanced data. This also allows to employ a lightweight and efficient 3D DCNN in terms of the number of filters and reserve memory resources for training the binary networks on full-scale and full-resolution 3D MRI volumes instead of 2D/3D patches or 2D slices. Thus, the proposed framework can encapsulate the spatial contiguity in each dimension and enhance context-awareness. The experimental results demonstrate the superiority of the proposed framework over the baseline method both qualitatively and quantitatively without additional labeling cost for manual labeling.

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