Buchumschlag

Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection

Gespeichert in:
Veröffentlicht in:arXiv.org (Dec 4, 2024), p. n/a
1. Verfasser: Kc, Prabhat
Weitere Verfasser: Zeng, Rongping
Veröffentlicht:
Cornell University Library, arXiv.org
Schlagworte:
Visual tasks
Deep learning
Visual perception
Image segmentation
Computer vision
Scene analysis
Computed tomography
Noise reduction
Medical imaging
Image classification
Image acquisition
Algorithms
Image quality
Object recognition
Machine learning
Visual perception driven algorithms
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Abstract:The remarkable success of deep learning methods in solving computer vision problems, such as image classification, object detection, scene understanding, image segmentation, etc., has paved the way for their application in biomedical imaging. One such application is in the field of CT image denoising, whereby deep learning methods are proposed to recover denoised images from noisy images acquired at low radiation. Outputs derived from applying deep learning denoising algorithms may appear clean and visually pleasing; however, the underlying diagnostic image quality may not be on par with their normal-dose CT counterparts. In this work, we assessed the image quality of deep learning denoising algorithms by making use of visual perception- and data fidelity-based task-agnostic metrics (like the PSNR and the SSIM) - commonly used in the computer vision - and a task-based detectability assessment (the LCD) - extensively used in the CT imaging. When compared against normal-dose CT images, the deep learning denoisers outperformed low-dose CT based on metrics like the PSNR (by 2.4 to 3.8 dB) and SSIM (by 0.05 to 0.11). However, based on the LCD performance, the detectability using quarter-dose denoised outputs was inferior to that obtained using normal-dose CT scans.
ISSN:2331-8422
DOI:10.1109/NSS/MIC/RTSD57108.2024.10658147
Quelle:Engineering Database