A new method for the automatic retrieval of medical cases based on the RadLex ontology.
OBJECTIVE
The goal of medical case-based image retrieval (M-CBIR) is to assist radiologists in the clinical decision-making process by finding medical cases in large archives that most resemble a given case. Cases are described by radiology reports comprised of radiological images and textual information on the anatomy and pathology findings. The textual information, when available in standardized terminology, e.g., the RadLex ontology, and used in conjunction with the radiological images, provides a substantial advantage for M-CBIR systems.


METHODS
We present a new method for incorporating textual radiological findings from medical case reports in M-CBIR. The input is a database of medical cases, a query case, and the number of desired relevant cases. The output is an ordered list of the most relevant cases in the database. The method is based on a new case formulation, the Augmented RadLex Graph and an Anatomy-Pathology List. It uses a new case relatedness metric [Formula: see text] that prioritizes more specific medical terms in the RadLex tree over less specific ones and that incorporates the length of the query case.


RESULTS
An experimental study on 8 CT queries from the 2015 VISCERAL 3D Case Retrieval Challenge database consisting of 1497 volumetric CT scans shows that our method has accuracy rates of 82 and 70% on the first 10 and 30 most relevant cases, respectively, thereby outperforming six other methods.


CONCLUSIONS
The increasing amount of medical imaging data acquired in clinical practice constitutes a vast database of untapped diagnostically relevant information. This paper presents a new hybrid approach to retrieving the most relevant medical cases based on textual and image information.
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel. assaf.spanier@mail.huji.ac.il.|Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel. assaf.spanier@mail.huji.ac.il.
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http://orcid.org/0000-0001-5091-8002
AB
Spanier
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel.
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D
Cohen
The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram Campus, 91904, Jerusalem, Israel.
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L
Joskowicz
D000465:Algorithms; D001331:Automation; D000066491:Clinical Decision-Making; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional; D016247:Information Storage and Retrieval; D011871:Radiology; D011873:Radiology Information Systems; D014057:Tomography, X-Ray Computed
Medical Content-Based Image Retrieval; RadLex ontology; Relatedness; Similarity metric