Domain-Enriched Deep Network for Micro-CT Image Segmentation

Amirsaeed Yazdani, Nicholas B. Stephens, Venkateswararao Cherukuri, Timothy Ryan, Vishal Monga

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

Abstract

Micro-computer tomography (μCT) is increasingly used in Anthropology and Palaeontology to quantify the external and internal osteological characteristics of extant/extinct species. One of the challenging tasks on such data is the accurate segmentation of images into bone and non-bone classes. Many intensity-based segmentation approaches have been proposed to overcome this issue, moving from global-thresholding to robust (semi)automatic methods. However, researchers often resort to laborious manual segmentation when the intensity levels of bone and non-bone material are extremely similar. Recently, deep learning methods have been shown to outperform traditional approaches for image segmentation. Here we propose a novel domain enriched deep network architecture that combines the benefits of deep learning with expert knowledge of bone structure via two components-1) a representation network capable of extracting features that are more responsive to bone structures and less responsive to non-bone structures, and 2) a segmentation network that utilizes the features obtained from the representation network to perform segmentation. Effective representation filters are obtained through a robust discriminative-features constraint that enables the discovery of novel features and enhances segmentation accuracy. Experiments performed on challenging μCT images of archaeological bones reveal practical merits of our proposal over state-of-the-art alternatives.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1867-1871
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Domain-Enriched Deep Network for Micro-CT Image Segmentation'. Together they form a unique fingerprint.

  • Cite this

    Yazdani, A., Stephens, N. B., Cherukuri, V., Ryan, T., & Monga, V. (2019). Domain-Enriched Deep Network for Micro-CT Image Segmentation. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 1867-1871). [9048654] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF44664.2019.9048654