@inbook {bCasamitjana18, title = {Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation}, booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017}, volume = { 10670}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {381-391}, publisher = {Springer}, organization = {Springer}, edition = {Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)}, address = {Cham}, abstract = {

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.

}, isbn = {978-3-319-75238-9}, issn = {978-3-319-75237-2}, doi = {https://doi.org/10.1007/978-3-319-75238-9_33}, url = {https://link.springer.com/chapter/10.1007/978-3-319-75238-9_33}, author = {Adri{\`a} Casamitjana and Marcel Cat{\`a} and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cCata, title = {Masked V-Net: an approach to brain tumor segmentation}, booktitle = {Multimodal Brain Tumor Segmentation Benchmark, Brain-lesion Workshop, MICCAI 2017 }, year = {2017}, month = {09/2017}, author = {Marcel Cat{\`a} and Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @mastersthesis {xCata16, title = {Feature Selection Methods for Predicting Pre-Clinical Stage in Alzheimer{\textquoteright}s Disease}, year = {2016}, abstract = {

Advisors: Ver{\'o}nica Vilaplana, Adri{\`a} Casamitjana

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Alzheimer{\textquoteright}s disease is still an incurable disease. Nevertheless, some of its biomarkers suffer changes in the early stages of the disease, long before clinical symptoms appear. In order to determine how biomarkers obtained from magnetic resonance (MRI) techniques affect the disease{\textquoteright}s evolution, machine learning techniques have been used to design and implement a classification system so as to predict the stages in which several patients belong. One of the main objectives of this project is reducing the number of data to manage, since MRI provide a large volume of data for each patient. As a result, we will focus on the stage of reduction and extraction of characteristics of the classifier which may be relevant for the mentioned problem. We will carry out an exhaustive analysis of different methods of selection of features to apply to biomedical data related to Alzheimer{\textquoteright}s disease. Results obtained will also be applicable to other fields. Finally, we will assess these methods with a multimodal data base provided by the collaboration agreement with Pasqual Maragall Foundation (FPM).

}, author = {Marcel Cat{\`a}}, editor = {Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana} }