Automatic lesion detection for narrow-band imaging bronchoscopy

Vahid Daneshpajooh; Danish Ahmad; Jennifer Toth; Rebecca Bascom; William E. Higgins

doi:10.1117/1.JMI.11.3.036002

30 May 2024 Automatic lesion detection for narrow-band imaging bronchoscopy

Vahid Daneshpajooh, Danish Ahmad, Jennifer Toth, Rebecca Bascom, William E. Higgins

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 3, 036002 (May 2024). https://doi.org/10.1117/1.JMI.11.3.036002

Abstract

Purpose

Early detection of cancer is crucial for lung cancer patients, as it determines disease prognosis. Lung cancer typically starts as bronchial lesions along the airway walls. Recent research has indicated that narrow-band imaging (NBI) bronchoscopy enables more effective bronchial lesion detection than other bronchoscopic modalities. Unfortunately, NBI video can be hard to interpret because physicians currently are forced to perform a time-consuming subjective visual search to detect bronchial lesions in a long airway-exam video. As a result, NBI bronchoscopy is not regularly used in practice. To alleviate this problem, we propose an automatic two-stage real-time method for bronchial lesion detection in NBI video and perform a first-of-its-kind pilot study of the method using NBI airway exam video collected at our institution.

Approach

Given a patient’s NBI video, the first method stage entails a deep-learning-based object detection network coupled with a multiframe abnormality measure to locate candidate lesions on each video frame. The second method stage then draws upon a Siamese network and a Kalman filter to track candidate lesions over multiple frames to arrive at final lesion decisions.

Results

Tests drawing on 23 patient NBI airway exam videos indicate that the method can process an incoming video stream at a real-time frame rate, thereby making the method viable for real-time inspection during a live bronchoscopic airway exam. Furthermore, our studies showed a 93% sensitivity and 86% specificity for lesion detection; this compares favorably to a sensitivity and specificity of 80% and 84% achieved over a series of recent pooled clinical studies using the current time-consuming subjective clinical approach.

Conclusion

The method shows potential for robust lesion detection in NBI video at a real-time frame rate. Therefore, it could help enable more common use of NBI bronchoscopy for bronchial lesion detection.

Citation Download Citation

Vahid Daneshpajooh, Danish Ahmad, Jennifer Toth, Rebecca Bascom, and William E. Higgins "Automatic lesion detection for narrow-band imaging bronchoscopy," Journal of Medical Imaging 11(3), 036002 (30 May 2024). https://doi.org/10.1117/1.JMI.11.3.036002

Received: 22 August 2023; Accepted: 14 May 2024; Published: 30 May 2024

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KEYWORDS

Video

Object detection

Education and training

Bronchoscopy

Cancer detection

Endoscopy

Lung cancer

Purpose

Approach

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Conclusion

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