Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen

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Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration ysicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates m

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Volume Preserving Brain Lesion Segmentation brain lesion segmentation due to its accuracy and efficiency. CNNs are generally trained with loss functions that measure the segmentation accuracy, such as the cross entropy loss and Dice loss. However, lesion load is a crucial measurement for disease analysis, and these loss functions do not guar

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Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Prill unknown. The objective of this study was to evaluate morphometric and microstructural properties based on structural and diffusion magnetic resonance imaging (dMRI) data in these MS phenotypes, and verify if selective intra-pathological alterations characterise GM structures. Diffusion Tensor Im

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Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathologyor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patient’s brain to a normal one. Many patient images are associated with irregularly distributed lesions, resu

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Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentationze that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temp

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