2022/02/08 by José V. Manjón, José E. Romero, Manjon, Jose V. +11
Computer Science · Neuroscience · #Brain Tumor Detection and Classification #FOS: Biological sciences #FOS: Electrical engineering #Image Retrieval and Classification Techniques #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Quantitative Methods (q-bio.QM) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2202.03920
openalex publication_date 2022/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resources for both clinical and research environments. In the last years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labelling at multiscale level and to deal with brain anatomical alterations such as white matter lesions. In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis which densely labels (N>100) the brain while being robust to the presence of white matter lesions. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in few minutes. We have deployed our new pipeline within our platform volBrain (www.volbrain.upv.es) which has been already demonstrated to be an efficient and effective manner to share our technology with users worldwide