Robustness evaluation of CAD systems for lung nodule segmentation using clinically relevant image perturbations

Fidan Mammadli; Terese A. E. Hellström; Christiaan G. A. Viviers; Igor Jacobs; Lotte J. S. Ewals; Nick Tasios; Dimitrios Mavroeidis; Hendrika P. M. Verhees; Peter H. N. de With; Joost Nederend; Fons van der Sommen

doi:10.1117/12.3006211

2 April 2024 Robustness evaluation of CAD systems for lung nodule segmentation using clinically relevant image perturbations

Fidan Mammadli, Terese A. E. Hellström, Christiaan G. A. Viviers, Igor Jacobs, Lotte J. S. Ewals, Nick Tasios, Dimitrios Mavroeidis, Hendrika P. M. Verhees, Peter H. N. de With, Joost Nederend, Fons van der Sommen

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292628 (2024) https://doi.org/10.1117/12.3006211
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Lung cancer has both high incidence and mortality rates compared to other cancer types. One important factor for improved patient survival is early detection. Deep learning for lung nodule detection has been extensively studied, as a tool to facilitate clinicians with early nodule detection and classification. Many publications are reporting high detection accuracy and several models have been introduced to clinical practice. However, certain models may have reduced performance in real-world clinical practice. In this study, we introduce a method to assess the robustness of lung nodule detection models. Medically relevant image perturbations are used to assess the robustness of these models. The perturbations include noise and motion perturbations, which have been created in consultation with an expert radiologist to ensure the clinical relevance of the artifacts for thoracic computed tomography (CT) scans. The evaluated models demonstrate robustness to clinically relevant noise simulations, but it shows less resilience to motion artifacts in perturbed CT scans. This robustness evaluation method, incorporating simulated relevant artifacts, can be extended for use in other applications involving the analysis of CT scans.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Fidan Mammadli, Terese A. E. Hellström, Christiaan G. A. Viviers, Igor Jacobs, Lotte J. S. Ewals, Nick Tasios, Dimitrios Mavroeidis, Hendrika P. M. Verhees, Peter H. N. de With, Joost Nederend, and Fons van der Sommen "Robustness evaluation of CAD systems for lung nodule segmentation using clinically relevant image perturbations", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292628 (2 April 2024); https://doi.org/10.1117/12.3006211

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