Automatic 8-tissue Segmentation for 6-month Infant Brains
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910579478757376 |
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| author | Dong, Yilan Kyriakopoulou, Vanessa Grigorescu, Irina McAlonan, Grainne Batalle, Dafnis Deprez, Maria |
| author_facet | Dong, Yilan Kyriakopoulou, Vanessa Grigorescu, Irina McAlonan, Grainne Batalle, Dafnis Deprez, Maria |
| contents | Numerous studies have highlighted that atypical brain development, particularly during infancy and toddlerhood, is linked to an increased likelihood of being diagnosed with a neurodevelopmental condition, such as autism. Accurate brain tissue segmentations for morphological analysis are essential in numerous infant studies. However, due to ongoing white matter (WM) myelination changing tissue contrast in T1- and T2-weighted images, automatic tissue segmentation in 6-month infants is particularly difficult. On the other hand, manual labelling by experts is time-consuming and labor-intensive. In this study, we propose the first 8-tissue segmentation pipeline for six-month-old infant brains. This pipeline utilizes domain adaptation (DA) techniques to leverage our longitudinal data, including neonatal images segmented with the neonatal Developing Human Connectome Project structural pipeline. Our pipeline takes raw 6-month images as inputs and generates the 8-tissue segmentation as outputs, forming an end-to-end segmentation pipeline. The segmented tissues include WM, gray matter (GM), cerebrospinal fluid (CSF), ventricles, cerebellum, basal ganglia, brainstem, and hippocampus/amygdala. Cycle-Consistent Generative Adversarial Network (CycleGAN) and Attention U-Net were employed to achieve the image contrast transformation between neonatal and 6-month images and perform tissue segmentation on the synthesized 6-month images (neonatal images with 6-month intensity contrast), respectively. Moreover, we incorporated the segmentation outputs from Infant Brain Extraction and Analysis Toolbox (iBEAT) and another Attention U-Net to further enhance the performance and construct the end-to-end segmentation pipeline. Our evaluation with real 6-month images achieved a DICE score of 0.92, an HD95 of 1.6, and an ASSD of 0.42. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_15198 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic 8-tissue Segmentation for 6-month Infant Brains Dong, Yilan Kyriakopoulou, Vanessa Grigorescu, Irina McAlonan, Grainne Batalle, Dafnis Deprez, Maria Image and Video Processing Artificial Intelligence Machine Learning Numerous studies have highlighted that atypical brain development, particularly during infancy and toddlerhood, is linked to an increased likelihood of being diagnosed with a neurodevelopmental condition, such as autism. Accurate brain tissue segmentations for morphological analysis are essential in numerous infant studies. However, due to ongoing white matter (WM) myelination changing tissue contrast in T1- and T2-weighted images, automatic tissue segmentation in 6-month infants is particularly difficult. On the other hand, manual labelling by experts is time-consuming and labor-intensive. In this study, we propose the first 8-tissue segmentation pipeline for six-month-old infant brains. This pipeline utilizes domain adaptation (DA) techniques to leverage our longitudinal data, including neonatal images segmented with the neonatal Developing Human Connectome Project structural pipeline. Our pipeline takes raw 6-month images as inputs and generates the 8-tissue segmentation as outputs, forming an end-to-end segmentation pipeline. The segmented tissues include WM, gray matter (GM), cerebrospinal fluid (CSF), ventricles, cerebellum, basal ganglia, brainstem, and hippocampus/amygdala. Cycle-Consistent Generative Adversarial Network (CycleGAN) and Attention U-Net were employed to achieve the image contrast transformation between neonatal and 6-month images and perform tissue segmentation on the synthesized 6-month images (neonatal images with 6-month intensity contrast), respectively. Moreover, we incorporated the segmentation outputs from Infant Brain Extraction and Analysis Toolbox (iBEAT) and another Attention U-Net to further enhance the performance and construct the end-to-end segmentation pipeline. Our evaluation with real 6-month images achieved a DICE score of 0.92, an HD95 of 1.6, and an ASSD of 0.42. |
| title | Automatic 8-tissue Segmentation for 6-month Infant Brains |
| topic | Image and Video Processing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.15198 |