Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation

Takaaki Sugino, Holger R. Roth, Masahiro Oda, Seiji Omata, Shinya Sakuma, Fumihito Arai, Kensaku Mori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A surgical simulator with elaborate artificial eyeball models has been developed for ophthalmic surgeries, in which sophisticated skills are required. To create the elaborate eyeball models with microstructures included in an eyeball, a database of eyeball models should be compiled by segmenting eye structures based on high-resolution medical images. Therefore, this paper presents an automated segmentation of eye structures from micro-CT images by using Fully Convolutional Networks (FCNs). In particular, we aim to construct a method for accurately segmenting eye structures from sparse annotation data. This method performs end-to-end segmentation of eye structures, including a workflow from training the FCN based on sparse annotation to obtaining the segmentation of the entire eyeball. We use the FCN trained on the slices sparsely annotated in a micro-CT volume to segment the remaining slices in the same volume. To achieve accurate segmentation from less annotated images, the multi-class segmentation is performed by using the network trained on the preprocessed and augmented micro-CT images; in the preprocessing, we apply filters for removing ring artifacts and random noises to the images, while in the data augmentation process, rotation and elastic deformation operations are performed on the sparsely-annotated training data. From the results of experiments for evaluating segmentation performances based on sparse annotation, we found that the FCN trained with data augmentation could achieve high segmentation accuracy of more than 90% even from a sparse training subset of only 2.5% of all slices.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510616455
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10578
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityHouston
Period2/11/182/13/18

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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