Publication detail

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

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

Original Title

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

English Title

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

Type

journal article

Language

en

Original Abstract

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.

English abstract

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.

Keywords

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

Released

15.02.2019

Publisher

MDPI

Pages from

1

Pages to

17

Pages count

17

URL

Full text in the Digital Library

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",
  annote="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.",
  address="MDPI",
  chapter="155280",
  doi="10.3390/app9030404",
  howpublished="online",
  institution="MDPI",
  number="3",
  volume="9",
  year="2019",
  month="february",
  pages="1--17",
  publisher="MDPI",
  type="journal article"
}