A B Spanier1,2, N Caplan3, J Sosna3, B Acar4, L Joskowicz5. 1. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem Givat Ram Campus, Jerusalem, 91904, Israel. assaf.spanier@mail.huji.ac.il. 2. Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel. assaf.spanier@mail.huji.ac.il. 3. Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel. 4. Department of Electrical-Electronics Engineering, Bogazici University, Istanbul, Turkey. 5. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem Givat Ram Campus, Jerusalem, 91904, Israel.
Abstract
PURPOSE: The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. METHODS: We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. RESULTS: Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. CONCLUSIONS: Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.
PURPOSE: The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. METHODS: We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. RESULTS: Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. CONCLUSIONS: Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.
Entities:
Keywords:
Annotations; Image features; Liver lesions; Medical content-based image retrieval
Authors: Sandy A Napel; Christopher F Beaulieu; Cesar Rodriguez; Jingyu Cui; Jiajing Xu; Ankit Gupta; Daniel Korenblum; Hayit Greenspan; Yongjun Ma; Daniel L Rubin Journal: Radiology Date: 2010-05-26 Impact factor: 11.105
Authors: Sharyn L S MacDonald; Ian A Cowan; Richard Floyd; Stuart Mackintosh; Rob Graham; Emma Jenkins; Richard Hamilton Journal: J Med Imaging Radiat Oncol Date: 2013-07-12 Impact factor: 1.735