Computer-aided detection and segmentation of malignant melanoma lesions on whole-body 18F-FDG PET/CT using an interpretable deep learning approach. (June 2022) - Dirks, Ine; Keyaerts, Marleen; Neyns, Bart; Vandemeulebroucke, Jef
Highlights: Development of an automated method for lesion detection and segmentation. An interpretable method to facilitate interaction with the medical doctor. Image analysis of whole-body 18F-FDG PET/CT. Automated localization of liver volume of interest. False positive reduction by a convolutional neural network. Abstract: Background and objective: In oncology, 18-fluorodeoxyglucose ( 18 F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to identify and analyse metabolically-active tumours. The combination of the high sensitivity and specificity from 18 F-FDG PET and the high resolution from CT makes accurate assessment of disease status and treatment response possible. Since cancer is a systemic disease, whole-body imaging is of high interest. Moreover, whole-body metabolic tumour burden is emerging as a promising new biomarker predicting outcome for innovative immunotherapy in different tumour types. However, this comes with certain challenges such as the large amount of data for manual reading, different appearance of lesions across the body and cumbersome reporting, hampering its use in clinical routine. Automation of the reading can facilitate the process, maximise the information retrieved from the images and support clinicians in making treatment decisions. Methods: This work proposes a fully automated system for lesion detection and segmentation on whole-body 18 F-FDG PET/CT. The novelty of the method stems from the fact that the same two-step approach used when manually reading the images was adopted, consisting of an intensity-based thresholding on PET followed by a classification that specifies which regions represent normal physiological uptake and which are malignant tissue. The dataset contained 69 patients treated for malignant melanoma. Baseline and follow-up scans together offered 267 images for training and testing. Results: On an unseen dataset of 53 PET/CT images, a median F1-score of 0.7500 was achieved with, on average, 1.566 false positive lesions per scan. Metabolically-active tumours were segmented with a median dice score of 0.8493 and absolute volume difference of 0.2986 ml. Conclusions: The proposed fully automated method for the segmentation and detection of metabolically-active lesions on whole-body 18 F-FDG PET/CT achieved competitive results. Moreover, it was compared to a direct segmentation approach which it outperformed for all metrics.