Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation

Tsubasa Konno, Takahiro Ninomiya, Kanta Miura, Koichi Ito, Noriko Himori, Parmanand Sharma, Toru Nakazawa, Takafumi Aoki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Major retinal layer segmentation methods from OCT images assume that the retina is flattened in advance, and thus cannot always deal with retinas that have changes in retinal structure due to ophthalmopathy and/or curvature due to myopia. To eliminate the use of flattening in retinal layer segmentation for practicality of such methods, we propose novel data augmentation methods for OCT images. Formula-driven data augmentation (FDDA) emulates a variety of retinal structures by vertically shifting each column of the OCT images according to a given mathematical formula. We also propose partial retinal layer copying (PRLC) that copies a part of the retinal layers and pastes it into a region outside the retinal layers. Through experiments using the OCT MS and Healthy Control dataset and the Duke Cyst DME dataset, we demonstrate that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina.

Original languageEnglish
Title of host publicationOphthalmic Medical Image Analysis - 11th International Workshop, OMIA 2024, Held in Conjunction with MICCAI 2024, Proceedings
EditorsAntony Bhavna, Hao Chen, Huihui Fang, Huazhu Fu, Cecilia S. Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Pages136-145
Number of pages10
ISBN (Print)9783031731181
DOIs
Publication statusPublished - 2025
Event11th International Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024 - Marrakesh, Morocco
Duration: 2024 Oct 102024 Oct 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume15188 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Workshop on Ophthalmic Medical Image Analysis, OMIA-XI 2024 was held in conjunction with the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
Country/TerritoryMorocco
CityMarrakesh
Period24/10/1024/10/10

Keywords

  • data augmentation
  • OCT
  • retinal layer
  • segmentation

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