Abstract
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 labeling 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.
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Disclosure of Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Acknowledgments
This work was supported by King’s-China Scholarship Council (K-CSC) (grant number 202008060109). The results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. The authors acknowledge support in part from the Wellcome Engineering and Physical Sciences Research Council (EPSRC) Centre for Medical Engineering at Kings College London (grant number WT 203148/Z/16/Z), and the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the funders, the NHS, the National Institute for Health Research, the Department of Health and Social Care, or the IHI-JU2. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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Dong, Y., Kyriakopoulou, V., Grigorescu, I., McAlonan, G., Batalle, D., Deprez, M. (2025). Automatic 8-Tissue Segmentation for 6-Month Infant Brains. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2024. Lecture Notes in Computer Science, vol 14747. Springer, Cham. https://doi.org/10.1007/978-3-031-73260-7_6
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DOI: https://doi.org/10.1007/978-3-031-73260-7_6
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