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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルMedical Imaging 2018
ホスト出版物のサブタイトルBiomedical Applications in Molecular, Structural, and Functional Imaging
編集者Barjor Gimi, Andrzej Krol
出版社SPIE
ISBN(電子版)9781510616455
DOI
出版ステータス出版済み - 2018
外部発表はい
イベントMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, 米国
継続期間: 2 11 20182 13 2018

出版物シリーズ

名前Progress in Biomedical Optics and Imaging - Proceedings of SPIE
10578
ISSN(印刷版)1605-7422

会議

会議Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
国/地域米国
CityHouston
Period2/11/182/13/18

All Science Journal Classification (ASJC) codes

  • 電子材料、光学材料、および磁性材料
  • 生体材料
  • 原子分子物理学および光学
  • 放射線学、核医学およびイメージング

フィンガープリント

「Automatic segmentation of eyeball structures from micro-CT images based on sparse annotation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル