Obtaining the potential number of object models/atlases needed in medical image analysis

Ze Jin; Jayaram K. Udupa; Drew A. Torigian

doi:10.1117/12.2549827

16 March 2020 Obtaining the potential number of object models/atlases needed in medical image analysis

Ze Jin, Jayaram K. Udupa, Drew A. Torigian

Proceedings Volume 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling; 1131533 (2020) https://doi.org/10.1117/12.2549827
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor in a population? However, the number of models for different objects may be different, and at the image level, it may not be possible to infer the number of models needed for each object. So, the modified question to which we are now seeking an answer to in this paper is: How many models/ atlases are needed for optimally encoding prior information to address the differing body habitus factor for each object in a body region? To answer this question, we modified our method in the previous study for seeking the optimum grouping for a given population of images but focusing on the individual objects. We present our results on head and neck computed tomography (CT) scans of 298 patients.

Citation Download Citation

Ze Jin, Jayaram K. Udupa, and Drew A. Torigian "Obtaining the potential number of object models/atlases needed in medical image analysis", Proc. SPIE 11315, Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling, 1131533 (16 March 2020); https://doi.org/10.1117/12.2549827

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KEYWORDS

Image segmentation

Medical imaging

Computed tomography

Binary data

Esophagus

Image processing

Neck

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