arXiv subjects

Jose V. Manjon

Publications and source records attributed to Jose V. Manjon.

3 recordsLinked to original sources

arXiv2022

vol2Brain: A new online Pipeline for whole Brain MRI analysis

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

q-bio.QM↗

arXiv2020

DeepHIPS: A novel Deep Learning based Hippocampus Subfield Segmentation method

The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.

q-bio.QM↗

arXiv2019

MRI denoising using Deep Learning and Non-local averaging

This paper proposes a novel method for automatic MRI denoising that exploits last advances in deep learning feature regression and self-similarity properties of the MR images. The proposed method is a two-stage approach. In the first stage, an overcomplete patch-based convolutional neural network blindly removes the noise without specific estimation of the local noise variance to produce a preliminary estimation of the noise-free image. The second stage uses this preliminary denoised image as a guide image within a rotationally invariant non-local means filter to robustly denoise the original noisy image. The proposed approach has been compared with related state-of-the-art methods and showed competitive results in all the studied cases while being much faster than comparable filters. We present a denoising method that can be blindly applied to any type of MR image since it can automatically deal with both stationary and spatially varying noise patterns.

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