Biomedical Segmentation

Laura Daza
Catalina Gómez
Pablo Arbeláez

Collaboration with Silvana Castillo and Luis Carlos Rivera
Collaboration with Amazon Web Services


Biomedical images are useful for diagnosis, treatment, and follow-up of patients with diverse pathologies and conditions. AI-based methods are tools to analyze these images. However, this task requires the intervention of specialized medical personal, and the interpretation of the images is dependent on the perception and expertise of the specialist analyzing each image. Medical image analysis requires considerable processing times and significant use of computational resources. Due to the complexity of the data, the anatomical variations, different modalities, three-dimensionality, among other challenges of this data.
We focus on the segmentation task, i.e., to find and differentiate a structure of interest within an image. This structure could be an organ or a pathology and could be in multiple locations and have different morphologies and sizes. Thus, we are developing a general segmentation method that has an excellent performance in various tasks and is independent of the possible variations and modalities found in data. We are implementing our methodology using the data provided by the Medical Segmentation Decathlon. That is a challenge with ten different categories for different organs and pathologies.

Presentation Video

Towards Robust General Medical Image Segmentation

L. Daza, J. Perez, P. Arbelaez

MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention

The Medical Segmentation Decathlon

Michela, A. et al.

The Medical Segmentation Decathlon, 2020

Cerberus: A Multi-headed Network

L. Daza, C. Gómez, and P. Arbeláez

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

Learning to Segment Brain Tumors

L. Daza, C. Gómez, and P. Arbeláez

15th Internacional Conference on Medical Information Processing and Analysis (SIPAIM), 2019

Brain Tumor Segmentation and Parsing on MRIs using Multiresolution Neural Networks

LS. Castillo, LA. Daza, LC. Rivera, P. Arbeláez

Brain Lesion workshop of the medical Image Computing and Computer assisted Interventions Conference, 2017

Volumetric multimodality neural network for brain tumor segmentation

L.S. Castillo, L.A. Daza, L.C. Rivera and P. Arbeláez

13th International Conference on Medical Information Processing and Analysis (SIPAIM), 2017

"Ideas are easy. It's the execution of ideas that really separates the sheep from the goats" - Sue Grafton

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