Detecting bone lesions in X-ray under diverse acquisition conditions

Tal Zimbalist; Ronnie Rosen; Keren Peri-Hanania; Yaron Caspi; Bar Rinott; Carmel Zeltser-Dekel; Eyal Bercovich; Yonina C. Eldar; Shai Bagon

doi:10.1117/1.JMI.11.2.024502

19 March 2024 Detecting bone lesions in X-ray under diverse acquisition conditions

Tal Zimbalist, Ronnie Rosen, Keren Peri-Hanania, Yaron Caspi, Bar Rinott, Carmel Zeltser-Dekel, Eyal Bercovich, Yonina C. Eldar, Shai Bagon

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 2, 024502 (March 2024). https://doi.org/10.1117/1.JMI.11.2.024502

Abstract

Purpose

The diagnosis of primary bone tumors is challenging as the initial complaints are often non-specific. The early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. We propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging. First, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians, and imaging protocols. This diversity poses a major challenge to any automatic analysis method.

Approach

We propose training an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only.

Results

We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at a 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69.

Conclusions

The proposed preprocessing method enables effectively coping with the inherent diversity of radiographs acquired in HMOs and EDs.

Citation Download Citation

Tal Zimbalist, Ronnie Rosen, Keren Peri-Hanania, Yaron Caspi, Bar Rinott, Carmel Zeltser-Dekel, Eyal Bercovich, Yonina C. Eldar, and Shai Bagon "Detecting bone lesions in X-ray under diverse acquisition conditions," Journal of Medical Imaging 11(2), 024502 (19 March 2024). https://doi.org/10.1117/1.JMI.11.2.024502

Received: 3 July 2023; Accepted: 4 March 2024; Published: 19 March 2024

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KEYWORDS

Bone

Tumors

Cancer detection

Object detection

Radiography

Education and training

X-rays

Purpose

Approach

Results

Conclusions

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