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Deepseg: Abdominal Organ Segmentation Using Deep Convolutional Neural Networks

Måns Larsson (Institutionen för signaler och system) ; Yuhang Zhang (Institutionen för signaler och system) ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende)
SSBA (2016)
[Konferensbidrag, övrigt]

A fully automatic method for abdominal organ segmentation is presented. The method uses a robust initialization step based on a multi-atlas approach where the center of the organ is estimated together with a region of interest surrounding the center. As a second step a convolutional neural network performing pixelwise classification is applied. The convolutional neural network consists of several full 3D convolutional layers and takes two input features, which are designed to ensure both local and global consistency. Despite limited training data, our preliminary experimental results are on par with state-of-the-art approaches that have been developed over many years. More specifically the method is applied to the MICCAI2015 challenge “Multi-Atlas Labeling Beyond the Cranial Vault” in the free competition for organ segmentation in the abdomen. It achieved the best results for 3 out of the 13 organs with a total mean dice coefficient of 0.757 for all organs. Top score was achieved for the gallbladder, the aorta and the right adrenal gland.

Nyckelord: Segmentation, Medical Image Analysis, Convolutional Neural Networks



Denna post skapades 2017-01-10. Senast ändrad 2017-07-31.
CPL Pubid: 246744

 

Institutioner (Chalmers)

Institutionen för signaler och system (1900-2017)
Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)

Ämnesområden

Matematik
Medicinsk bildbehandling

Chalmers infrastruktur