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

6 Scopus citations


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
Number of pages5
ISBN (Electronic)9781728143002
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
ISSN (Print)1058-6393


Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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