Skip to content

New article: Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction

May 7, 2019

In recent years, the deep learning based methods achieved the state of the art for medical image segmentation. However, these methods are sensitive to image intensity distribution changes such as different MRI scanner or protocol. Therefore, a neural network has to be retrained to perform similarly in other datasets. In this paper, we investigated the use of transfer learning strategy to overcome the changes in the data distribution. We reduced the number of training images by leveraging the knowledge obtained by a pre-trained network and improved the training speed by reducing the number of trainable parameters of the CNN. We tested our method on two publicly available datasets – MICCAI 2012 and IBSR – and compared them with a commonly used approach: FIRST. Our method showed similar results to those obtained by a fully trained CNN, and our method used a remarkably smaller number of images from the target domain.

 

Article from Kaisar Kushibar “Supervised Domain Adaptation for Automatic Sub-cortical Brain Structure Segmentation with Minimal User Interaction” in Scientific Report journal.

 

https://www.nature.com/articles/s41598-019-43299-z 

 

Share it!

More News

lilianaMaiamasteer
November 9, 2022

Vicorob’s people: Liliana is a Colombian PhD student in medical imaging

Community

Automated Analysis of Magnetic Resonance Imaging of the Breast
May 5, 2015

Albert Gubern defends his PhD thesis “Automated Analysis of Magnetic Resonance Imaging of the Breast”

Medical Imaging Lab, News, Scientific Results

sextant II3
November 28, 2024

SEXTANT II: Una nova embarcació al servei de la comunitat científica

Underwater Robotics, Underwater Vision

MARTECH 2013
October 25, 2013

MARTECH 2013

News, Underwater Robotics, Underwater Vision