Multi-Class Micro-CT Image Segmentation Using Sparse Regularized Deep Networks
| Autor: | Nicholas B. Stephens, Vishal Monga, Yung-Chen Sun, Timothy M. Ryan, Amirsaeed Yazdani |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Feature extraction Computer Science - Computer Vision and Pattern Recognition Pattern recognition Dirt 02 engineering and technology Image segmentation Electrical Engineering and Systems Science - Image and Video Processing 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Discriminative model Market segmentation FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Segmentation Artificial intelligence Scale (map) business |
| Zdroj: | ACSSC |
| Popis: | 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 (micro-CT) 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 micro-CT segmentation. 5 pages, 6 figures, accepted in 2020 54th Asilomar Conference on Signals, Systems, and Computers |
| Databáze: | OpenAIRE |
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