Brain tumour segmentation


Diferent clases presents in the dataset.

One of the international contest where we have participated is the BRAT contest, the aim is to use multispectra resonance datasets carefully labeled by trained physician.

Every label corresponds to a diferent tumour stage, from beningn tumours to growing agresive, or necresed region and more.

One of the major contributions of our group was to provide enhanced methods for 3D convulational networs, this kind of networks performs very well when comparing the multiple layeres from the 3D image dataset, to progress in the tumour clasification, using the multisepectral filters to better detect lession.

2 Multispectral image showing diferent textures

Data clasification requieres the extra information providede by diferent RMS modalities.

Why this tumour segmentation is important?

The publication shows various patters, first of all is that in a very competitive enviromnent, most of the models still perform with clinical accuracy, the reason for this is related about the way pixel classification is evaluated. In this environment, we were able to construct better models by providing esnambling of all participants, showing for these case a performance close to 99.9% whihc basically means that at this stage, whith this image modality this can be considered as a solved problem.

Tumour segmentation helps to several clinical issues associated to diferent clinical aspects of this kind of analysis.

The location and relative position of lessions founds can determinate what is the most precisse treatment to apply, and what are the risks associated to every issue.

Main organizators

Publications & Scientific outputs

  • Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge  
  • 3D measurements in conventional X-ray imaging with RGB-D sensors  

    Volume: 42

    Page: 73-79

    DOI: 10.1016/j.medengphy.2017.01.024

  • Evaluation of modern camera calibration techniques for conventional diagnostic X-ray imaging settings  

    Volume: 10

    Page: 68-81

    DOI: 10.1007/s12194-016-0369-y