A new method for the automatic retrieval of medical cases based on the RadLex ontology

A. B. Spanier*, D. Cohen, L. Joskowicz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Purpose: 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 relCase 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.

Original languageAmerican English
Pages (from-to)471-484
Number of pages14
JournalInternational journal of computer assisted radiology and surgery
Volume12
Issue number3
DOIs
StatePublished - 1 Mar 2017

Bibliographical note

Funding Information:
This research was partially funded by Grant 53681 from the Israeli Ministry of Science and Technology.

Publisher Copyright:
© 2016, CARS.

Keywords

  • Medical Content-Based Image Retrieval
  • RadLex ontology
  • Relatedness
  • Similarity metric

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