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

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.

[1]  Niranjana Sampathila,et al.  K-means clustering approach for segmentation of corpus callosum from brain magnetic resonance images , 2014, International Conference on Circuits, Communication, Control and Computing.

[2]  Milan Sonka,et al.  Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples , 2000, IEEE Transactions on Medical Imaging.

[3]  Konstantinos Kamnitsas,et al.  Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation , 2016, Medical Image Anal..

[4]  S. Noachtar,et al.  Differences in corpus callosum volume and diffusivity between temporal and frontal lobe epilepsy , 2010, Epilepsy & Behavior.

[5]  Jinwoo Hong,et al.  Corpus callosum segmentation using deep neural networks with prior information from multi-atlas images , 2018, Medical Imaging.

[6]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[7]  Adel Said Elmaghraby,et al.  Variability of the relative corpus callosum cross sectional area between dyslexic and normally developed brains , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[8]  Alejandro F. Frangi,et al.  Active shape model segmentation with optimal features , 2002, IEEE Transactions on Medical Imaging.

[9]  Leticia Rittner,et al.  Corpus Callosum 2D Segmentation on Diffusion Tensor Imaging Using Growing Neural Gas Network , 2017 .

[10]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[11]  Thierry Blu,et al.  Efficient energies and algorithms for parametric snakes , 2004, IEEE Transactions on Image Processing.

[12]  Max A. Viergever,et al.  Automatic Segmentation of MR Brain Images With a Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[13]  Shuiwang Ji,et al.  Deep convolutional neural networks for multi-modality isointense infant brain image segmentation , 2015, NeuroImage.

[14]  Roberto de Alencar Lotufo,et al.  Watershed-Based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI , 2011, 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images.

[15]  John G. Csernansky,et al.  Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults , 2007, Journal of Cognitive Neuroscience.

[16]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Qingshan Liu,et al.  A novel learning based segmentation method for rodent brain structures using MRI , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[20]  N. Minshew,et al.  Corpus callosum size in autism , 2000, Neurology.

[21]  Tryphon T. Georgiou,et al.  A new distribution metric for image segmentation , 2008, SPIE Medical Imaging.

[22]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[23]  Daniel L. Rubin,et al.  Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions , 2017, Journal of Digital Imaging.

[24]  Arthur W. Toga,et al.  Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces , 2011, 2011 International Conference on Computer Vision.