Detail publikace

Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation

KOLAŘÍK, M. BURGET, R. UHER, V. ŘÍHA, K. DUTTA, M.

Originální název

Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

The 3D image segmentation is the process of partitioning a digital 3D volumes into multiple segments. This paper presents a fully automatic method for high resolution 3D volumetric segmentation of medical image data using modern supervised deep learning approach. We introduce 3D Dense-U-Net neural network architecture implementing densely connected layers. It has been optimized for graphic process unit accelerated high resolution image processing on currently available hardware (Nvidia GTX 1080ti). The method has been evaluated on MRI brain 3D volumetric dataset and CT thoracic scan dataset for spine segmentation. In contrast with many previous methods, our approach is capable of precise segmentation of the input image data in the original resolution, without any pre-processing of the input image. It can process image data in 3D and has achieved accuracy of 99.72% on MRI brain dataset, which outperformed results achieved by human expert. On lumbar and thoracic vertebrae CT dataset it has achieved the accuracy of 99.80%. The architecture proposed in this paper can also be easily applied to any task already using U-Net network as a segmentation algorithm to enhance its results. Complete source code was released online under open-source license.

Klíčová slova

3D segmentation; brain; deep learning; neural network; open-source; semantic segmentation; spine; u-net

Autoři

KOLAŘÍK, M.; BURGET, R.; UHER, V.; ŘÍHA, K.; DUTTA, M.

Vydáno

15. 2. 2019

Nakladatel

MDPI

ISSN

2076-3417

Periodikum

Applied Sciences - Basel

Ročník

9

Číslo

3

Stát

Švýcarská konfederace

Strany od

1

Strany do

17

Strany počet

17

URL

https://www.mdpi.com/2076-3417/9/3/404

Plný text v Digitální knihovně

http://hdl.handle.net/11012/179271

BibTex

@article{BUT155280,
  author="Martin {Kolařík} and Radim {Burget} and Václav {Uher} and Kamil {Říha} and Malay Kishore {Dutta}",
  title="Optimized High Resolution 3D Dense-U-Net Network for Brain and Spine Segmentation",
  journal="Applied Sciences - Basel",
  year="2019",
  volume="9",
  number="3",
  pages="1--17",
  doi="10.3390/app9030404",
  issn="2076-3417",
  url="https://www.mdpi.com/2076-3417/9/3/404"
}