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| Hauptseite > Workflowsammlungen > In Bearbeitung > Segmentation of the intracranial cavity in T1-weighted MR images of the human head by a location-specific 3D U-Net |
| Hauptseite > Workflowsammlungen > In Bearbeitung > Segmentation of the intracranial cavity in T1-weighted MR images of the human head by a location-specific 3D U-Net |
| Poster (After Call) | FZJ-2026-03336 |
; ;
2026
Abstract: The intracranial cavity (ICC) is a space in the head which contains the brain, cerebrospinal fluid (CSF), meninges and blood vessels. Its volume is often used as a reference variable in studies of brain structure in order to account for the inter-individual variability in brain size. However, the segmentation of the ICC in T1-weighted MR images is challenging because the visual appearance of its boundary is heterogenous depending on the location in the head, but also between images: E.g., the dorsal skull consists of several thin layers, which can differ in thickness and contrast, whereas the neck below the brain shows larger portions of fat tissue and muscles, and the brain can directly touch the surrounding meninges, but there might be also larger CSF filled spaces.Here we introduce a method for the ICC segmentation based on a convolutional neural network (CNN) with U-Net architecture to overcome these difficulties. All MR images were affinely registered to a reference brain image to reduce variability in global size and orientation, so that a data augmentation by geometrical transformations could be avoided. For the CNN training we used 98 T1 MR images from previous studies with manual ICC segmentation in every tenth sagittal section. Firstly, we trained a 2D U-Net CNN with the segmented sections to predict the ICC in the interleaving sections. This yielded 3D ICC masks, which were visually checked for correctness. Next, we used these 3D masks for the training of a 3D U-Net CNN. The images were partitioned in 27 overlapping tiles of length 96 voxels, and one model for each tile was trained, so that these were more specific for head parts. The binary predictions of models in overlapping parts of the tiles were averaged with weights depending on the distance to the tile centers.To validate this method, manual segmentations of 192 MR images (different from training images) where compared with their predicted segmentations. This yielded a mean Dice coefficient of 0.962 ± 0.017, and a mean relative mask size difference of 2.0 % per image. Next, ICC masks were predicted for 433 longitudinal image pairs of the 1000BRAINS study1 (mean time difference 3.7 ± 0.8 years), which yielded a mean relative ICC volume difference of 0.16 ± 0.28 %, showing the robustness of this method.The CNN trainings used an NVIDIA V100 GPU with 16 GB RAM, whereas the predictions were run within 11 sec per image on a QUADRO P2200 GPU (5 GB RAM).[1] Caspers S, et al (2014) Front Aging Neurosci 6
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