Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

A. Segawa; M. Nemoto; H. Kaida; Y. Kimura; T. Nagaoka; M. Katsuhiro; Y. Takahiro; H. Kohei; T. Tatsuya; K. Kazuhiro; I. Kazunari

doi:10.1117/12.3006643

2 April 2024 Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection

A. Segawa, M. Nemoto, H. Kaida, Y. Kimura, T. Nagaoka, M. Katsuhiro, Y. Takahiro, H. Kohei, T. Tatsuya, K. Kazuhiro, I. Kazunari

Author Affiliations +

Proceedings Volume 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 1293119 (2024) https://doi.org/10.1117/12.3006643
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

We propose an unsupervised method to detect lung lesions on FDG-PET/CT images based on deep image anomaly detection using 2.5-dimensional (2.5D) image processing. This 2.5D processing is applied to preprocessed FDG-PET/CT images without image patterns other than lung fields. It enhances lung lesions by parallel analysis of axial, coronal, and sagittal FDG-PET/CT slice images using multiple 2D U-Net. All the U-Nets are pretrained by 95 cases of normal FDG-PET/CT images having no lung lesions and used to transform CT slice images to normal FDG-PET slice images without any lesion-like SUV patterns. A lesion-enhanced image is obtained by merging subtractions of the transformed three normal FDG-PET images from the input FDG-PET image. Lesion detection is performed by simple binarization of the lesion-enhanced images. The threshold value varies from the case and is the 30-percentile voxel value of the target lesion-enhanced image. In each extracted region, the average of the intra-regional voxel values of the enhanced image is computed and assigned as a lesion-like score. We evaluated the proposed method by 27 patients FDG-PET/CT images with 41 lung lesions. The proposed method achieved 82.9 % of lesion detection sensitivity with five false positives per case. The result was significantly superior to the detection performance of FDG-PET image thresholding and indicates that the proposed method may be helpful for effective lung lesion detection. Future works include expanding the detectable range of lesions to outside lungs, such as mediastinum and axillae.

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

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A. Segawa, M. Nemoto, H. Kaida, Y. Kimura, T. Nagaoka, M. Katsuhiro, Y. Takahiro, H. Kohei, T. Tatsuya, K. Kazuhiro, and I. Kazunari "Unsupervised lung lesion detection on FDG-PET/CT images by deep image transformation-based 2.5-dimensional local anomaly detection", Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 1293119 (2 April 2024); https://doi.org/10.1117/12.3006643

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