Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks

Abstract

Purpose: 90Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ-at-risks is labor-intensive and time-consuming in 90Y SIRT planning. In this study, we developed a convolutional neural network (CNN)-based method for automated lungs, liver, and tumor segmentation on 99mTc-MAA SPECT/CT images for 90Y SIRT planning. Methods: 99mTc-MAA SPECT/CT images and corresponding clinical segmentations were retrospectively collected from 56 patients who underwent 90Y SIRT. The collected data were used to train three CNN-based segmentation algorithms for lungs, liver, and tumor segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), surface DSC, and average symmetric surface distance (ASSD). Dosimetric parameters (volume, counts, and lung shunt fraction) were measured from the segmentation results and were compared with clinical reference segmentations. Results: The evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97–0.98], 0.91 [0.83–0.93], and 0.85 [0.71–0.88]; surface DSCs of 0.99 [0.97–0.99], 0.86 [0.77–0.93], and 0.85 [0.62–0.93], and ASSDs of 0.91 [0.69–1.5], 4.8 [2.6-8.4], and 4.7 [3.5–9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX-2080Ti GPU. Conclusion: This work presents CNN-based algorithms to segment lungs, liver, and tumor from 99mTc-MAA SPECT/CT images. The results demonstrated the potential of the proposed CNN-based segmentation method for assisting 90Y SIRT planning while drastically reducing operator time.