โก Quick Summary
This study introduces a deep learning-based pipeline for segmenting the cerebral cortex laminar structure in histology images of the common marmoset. The proposed method outperformed existing techniques, achieving a Jaccard Index of 85.318% and a mean 95th percentile Hausdorff distance of 92.150 ฮผm.
๐ Key Details
- ๐ Dataset: Nissl-stained and myelin-stained slice images of the common marmoset
- โ๏ธ Technology: AI-based tools and a trained deep learning model
- ๐ Performance: Mean 95th percentile Hausdorff distance: 92.150 ฮผm
- ๐ Jaccard Index: 85.318%
๐ Key Takeaways
- ๐ง Understanding cortical layers is crucial for studying brain connectivity and neurological disorders.
- ๐ค A novel computational framework was developed to enhance segmentation accuracy.
- ๐ The pipeline achieved a Euclidean distance of 1274.750 ยฑ 156.400 ฮผm for cortical label acquisition.
- ๐ Compared to Wagstyl et al., this study showed improved segmentation quality.
- ๐ The study highlights the potential of deep learning in neuroimaging analysis.
- ๐ฌ The common marmoset is emerging as a valuable model in neuroscience research.
- ๐ Published in Neuroinformatics, this research contributes to the growing field of AI in neuroscience.
๐ Background
The cerebral cortex plays a vital role in processing information in the brain, and its laminar structure is essential for understanding neuronal connectivity. Traditional methods of analyzing these layers can be time-consuming and prone to human error. The integration of deep learning technologies offers a promising avenue for enhancing the accuracy and efficiency of this analysis.
๐๏ธ Study
This study focused on the common marmoset (Callithrix jacchus), a new world monkey that has gained popularity in neuroscience. Researchers utilized Nissl-stained and myelin-stained histology images to develop a deep learning-based pipeline for segmenting the cerebral cortex laminar structure. The framework involved acquiring cortical labels through AI tools followed by segmentation using a trained model.
๐ Results
The results demonstrated that the proposed pipeline achieved a mean 95th percentile Hausdorff distance of 92.150 ฮผm, outperforming the previous method by Wagstyl et al., which reported a mean 95HD of 94.170 ฮผm. Additionally, the Jaccard Index of 85.318% indicated superior segmentation quality compared to the 83.000% reported in earlier studies.
๐ Impact and Implications
The findings from this study have significant implications for the field of neuroscience. By improving the accuracy of cortical layer segmentation, researchers can gain deeper insights into the connectivity patterns of neurons and their roles in neurological disorders. This advancement could pave the way for better diagnostic tools and therapeutic strategies in the future.
๐ฎ Conclusion
This research highlights the transformative potential of deep learning in neuroimaging. The developed pipeline not only enhances the understanding of the cerebral cortex laminar structure but also sets a precedent for future studies utilizing AI technologies in neuroscience. Continued exploration in this area promises to unlock new frontiers in our understanding of the brain.
๐ฌ Your comments
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A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.
Abstract
Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of 1274.750 ยฑ 156.400 ฮผ m for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( 1800.630 ฮผ m ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean 95 th percentile Hausdorff distance (95HD) ofย 92.150 ฮผ m . Whereas a mean 95HD ofย 94.170 ฮผ m was obtained from Wagstyl et al. We also compared our pipeline’s performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, 85.318 % Jaccard Index acquired from our pipeline, while 83.000 % was stated in their paper.
Author: [‘Wang J’, ‘Gong R’, ‘Heidari S’, ‘Rogers M’, ‘Tani T’, ‘Abe H’, ‘Ichinohe N’, ‘Woodward A’, ‘Delmas PJ’]
Journal: Neuroinformatics
Citation: Wang J, et al. A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images. 2024; (unknown volume):(unknown pages). doi: 10.1007/s12021-024-09688-0