Opencc – an open Benchmark data set for Corpus Callosum Segmentation and Evaluation

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Opencc – an open Benchmark data set for Corpus Callosum Segmentation and Evaluation
Title:
Opencc – an open Benchmark data set for Corpus Callosum Segmentation and Evaluation
Journal Title:
2020 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
30 September 2020
Citation:
Yang, X., Zhao, X., Tjio, G., Chen, C., Wang, L., Wen, B., & Su, Y. (2020). Opencc – an open Benchmark data set for Corpus Callosum Segmentation and Evaluation. 2020 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip40778.2020.9191097
Abstract:
Neuroimaging studies have revealed that the structural changes of the corpus callosum (CC) are evident in a variety of neurological diseases, such as epilepsy and autism. Segmentation of the CC from magnetic resonance images (MRI) of the brain is a crucial step in the diagnosis of various brain disorders. However, the lack of open benchmark CC datasets has hindered development of CC segmentation techniques. In this work, we present an open benchmark dataset - OpenCC - for CC segmentation and evaluation. The dataset was built through alternative application of automatic segmentation and manual refinement. The automatic segmentation is based on recent advances in deep learning - fully convolutional networks, specifically U-Net, while the manual refinement is done by domain radiologists. The resulting dataset consists of 4643 mid-sagittal (or near mid-sagittal) slices and their corresponding CC masks. Furthermore, we provided some baseline segmentation results on the OpenCC dataset by using two latest deep learning segmentation approaches. The OpenCC dataset can be used for comparison and evaluation of newly developed CC segmentation algorithms. We endeavor that, through the publishing of the OpenCC dataset and baseline segmentation results, we could promote further development of CC segmentation techniques.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
978-1-7281-6396-3
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