Automated detection and quantification of prostatic PSMA uptake in SPECT/CT using a deep learning algorithm for segmentation of pelvic anatomy

作者：Karl Sjöstrand, Aseem Anand, Jens Richter, Kerstin Johnsson, Konrad Gjertsson, Lars Edenbrandt, Vivien Wong · 年份：2018 · 被引用次数：2 · 研究领域：Prostate Cancer Treatment and Research、Radiomics and Machine Learning in Medical Imaging、Radiopharmaceutical Chemistry and Applications

30 Objectives: 99mTc MIP-1404 (1404), a prostate-specific membrane antigen (PSMA) targeted imaging agent is currently under investigation for the detection of clinically significant disease in prostate cancer. Quantitative assessment of tracer uptake in SPECT/CT images requires substantial user interaction and introduces observer variability. Our objective was to develop a deep learning algorithm for automated detection and quantification of prostatic 1404 uptake in SPECT/CT images in a clinical setting. Methods: We developed a deep learning algorithm based on convolutional neural networks for automatically segmenting the prostate and pelvic bones from CT images. The algorithm was designed to process both high- and low-dose CT images as well as whole and part body field of views, with no manual interaction necessary. The training material consisted of 100 diagnostic CT images (all male) with complete and distinct segmentations, performed manually, for relevant anatomical regions. Validation of the algorithm was performed using SPECT/CT images from 102 high-risk prostate cancer patients in a phase 2 clinical study (MIP-1404-201, NCT01667536) who underwent 1404 imaging prior to radical prostatectomy. These scans were previously quantified manually using the OsiriX medical image viewer (Pixmeo SARL), by measuring the maximum uptake in a circular ROI placed inside the prostate in the slice and region with highest uptake values determined visually. The automated algorithm uses it...