Multi-Class Micro-CT Image Segmentation Using Sparse Regularized Deep Networks

Amirsaeed Yazdani, Yung Chen Sun, Nicholas B. Stephens, Timothy Ryan, Vishal Monga

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

1 Scopus citations


It is common in anthropology and paleontology to address questions about extant and extinct species through the quantification of osteological features observable in micro-computed tomographic (mCT) scans. In cases where remains were buried, the grey values present in these scans may be classified as belonging to air, dirt, or bone. While various intensity-based methods have been proposed to segment scans into these classes, it is often the case that intensity values for dirt and bone are nearly indistinguishable. In these instances, scientists resort to laborious manual segmentation, which does not scale well in practice when a large number of scans are to be analyzed. Here we present a new domain-enriched network for three-class image segmentation, which utilizes the domain knowledge of experts familiar with manually segmenting bone and dirt structures. More precisely, our novel structure consists of two components: 1) a representation network trained on special samples based on newly designed custom loss terms, which extracts discriminative bone and dirt features, 2) and a segmentation network that leverages these extracted discriminative features. These two parts are jointly trained in order to optimize the segmentation performance. A comparison of our network to that of the current state-of-the-art U-NETs demonstrates the benefits of our proposal, particularly when the number of labeled training images are limited, which is invariably the case for mCT segmentation.

Original languageEnglish (US)
Title of host publicationConference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9780738131269
StatePublished - Nov 1 2020
Event54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 - Pacific Grove, United States
Duration: Nov 1 2020Nov 5 2020

Publication series

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


Conference54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Country/TerritoryUnited States
CityPacific Grove

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications


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