Volumetric Segmentation and Characterisation of the Paracingulate Sulcus on MRI Scans

Many architectures of deep neural networks have been designed to solve specific biomedical problems, among which segmentation is a critical step to detect and locate the boundaries of the target object from an image. In this paper, we develop a deep learning based framework to automatically segment the paracingulate sulcus (PCS) from the MRI scan and estimate lengths for its segments. The study is the first work on segmentation and characterisation of the PCS, and the model achieves a Dice score of over 0.77 on segmentation, which demonstrates its potential for clinical use to assist human annotation. Moreover, the proposed architecture as a solution can be generalised to other problems where the object has similar patterns.

[1]  Charles Fernyhough,et al.  Paracingulate sulcus morphology is associated with hallucinations in the human brain , 2015, Nature Communications.

[2]  Xi Chen,et al.  Deep contextual residual network for electron microscopy image segmentation in connectomics , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[3]  T. Crow,et al.  Paracingulate sulcus asymmetry; Sex difference, correlation with semantic fluency and change over time in adolescent onset psychosis , 2010, Psychiatry Research: Neuroimaging.

[4]  Lin Yang,et al.  Coarse-to-Fine Stacked Fully Convolutional Nets for lymph node segmentation in ultrasound images , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  S. Dwivedi,et al.  Obesity May Be Bad: Compressed Convolutional Networks for Biomedical Image Segmentation , 2020 .

[6]  O. Etard,et al.  Sulcal Polymorphisms of the IFC and ACC Contribute to Inhibitory Control Variability in Children and Adults , 2018, eNeuro.

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

[8]  P. Maruff,et al.  Hemispheric and gender-related differences in the gross morphology of the anterior cingulate/paracingulate cortex in normal volunteers: an MRI morphometric study. , 2001, Cerebral cortex.

[9]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[10]  S. Pappatà,et al.  Paracingulate sulcus morphology in men with early-onset schizophrenia , 2003, British Journal of Psychiatry.

[11]  H. Benali,et al.  BrainVISA: Software platform for visualization and analysis of multi-modality brain data , 2001, NeuroImage.

[12]  Alan C. Evans,et al.  Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map. , 1996, Cerebral cortex.

[13]  Feng Yang,et al.  A Multi-Stage Framework With Context Information Fusion Structure For Skin Lesion Segmentation , 2018, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Hao Chen,et al.  DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Dennis Velakoulis,et al.  The influence of sulcal variability on morphometry of the human anterior cingulate and paracingulate cortex , 2006, NeuroImage.

[17]  Thomas Brox,et al.  3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation , 2016, MICCAI.