CT Prostate Segmentation Based on Synthetic MRI-aided Deep Attention Fully Convolution Network.

PURPOSE Accurate segmentation of the prostate on computed tomography (CT) for treatment planning is challenging due to CT's poor soft-tissue contrast. Magnetic resonance imaging (MRI) has been used to aid prostate delineation, but its final accuracy is limited by MRI-CT registration errors. We developed a deep attention-based segmentation strategy on CT-based synthetic MRI (sMRI) to deal with the CT prostate delineation challenge without MRI acquisition. METHODS AND MATERIALS We developed a prostate segmentation strategy which employs an sMRI-aided deep attention network to accurately segment the prostate on CT. Our method consists of three major steps. First, a cycle generative adversarial network was used to estimate an sMRI from CT images. Second, a deep attention fully convolution network was trained based on sMRI and the prostate contours deformed from MRIs. Attention models were introduced to pay more attention to prostate boundary. The prostate contour for a query patient was obtained by feeding the patient's CT images into the trained sMRI generation model and segmentation model. RESULTS The segmentation technique was validated with a clinical study of 49 patients by leave-one-out experiments and validated with an additional 50 patients by hold-out test. The Dice similarity coefficient, Hausdorff distance and mean surface distance indices between our segmented and deformed MRI-defined prostate manual contours were 0.92±0.09, 4.38±4.66 mm, and 0.62±0.89 mm, respectively, with leave-one-out experiments, and were 0.91±0.07, 4.57±3.03 mm, and 0.62±0.65 mm, respectively, with hold-out test. CONCLUSION We have proposed a novel CT-only prostate segmentation strategy using CT-based sMRI, and validated its accuracy against the prostate contours that were manually drawn on MRI images and deformed to CT images. This technique could provide accurate prostate volume for treatment planning without requiring MRI acquisition, greatly facilitating the routine clinical workflow.

[1]  Yaozong Gao,et al.  Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests , 2016, IEEE Transactions on Medical Imaging.

[2]  Yinghuan Shi,et al.  A learning-based CT prostate segmentation method via joint transductive feature selection and regression , 2016, Neurocomputing.

[3]  Desire Sidibé,et al.  A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images , 2012, Comput. Methods Programs Biomed..

[4]  Ben Glocker,et al.  Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images , 2018, Medical Image Anal..

[5]  C. S. Kalyani,et al.  Segmentation of rectum from CT images using K-means clustering for the EBRT of prostate cancer , 2016, 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT).

[6]  M van Herk,et al.  Definition of the prostate in CT and MRI: a multi-observer study. , 1999, International journal of radiation oncology, biology, physics.

[7]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Yang Lei,et al.  A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study , 2019, Journal of Nuclear Cardiology.

[9]  Xiaofeng Yang,et al.  Improved prostate delineation in prostate HDR brachytherapy with TRUS‐CT deformable registration technology: A pilot study with MRI validation , 2017, Journal of applied clinical medical physics.

[10]  Aaron Carass,et al.  Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN , 2018, DLMIA/ML-CDS@MICCAI.

[11]  Joseph O Deasy,et al.  Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an introduction to the scientific issues. , 2010, International journal of radiation oncology, biology, physics.

[12]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[13]  Tian Liu,et al.  Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation , 2019, Medical physics.

[14]  A. Jemal,et al.  Cancer treatment and survivorship statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[15]  Steve B. Jiang,et al.  Fully automated organ segmentation in male pelvic CT images , 2018, Physics in medicine and biology.

[16]  Peter L Choyke,et al.  Imaging prostate cancer: a multidisciplinary perspective. , 2007, Radiology.

[17]  Peng Li,et al.  Efficient and Low-Cost Deep-Learning Based Gaze Estimator for Surgical Robot Control , 2018, 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR).

[18]  Yinghuan Shi,et al.  Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Yang Lei,et al.  Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy. , 2019, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[20]  Qing Chang,et al.  Texture analysis method for shape-based segmentation in medical image , 2011, 2011 4th International Congress on Image and Signal Processing.

[21]  Yang Lei,et al.  MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. , 2019, Medical physics.

[22]  Yang Lei,et al.  Multiparametric MRI-guided dose boost to dominant intraprostatic lesions in CT-based High-dose-rate prostate brachytherapy. , 2019, The British journal of radiology.

[23]  Oscar Acosta,et al.  Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[24]  Steve B. Jiang,et al.  Segmentation of the prostate and organs at risk in male pelvic CT images using deep learning , 2018, Biomedical Physics & Engineering Express.

[25]  Wendy L. Smith,et al.  Prostate volume contouring: a 3D analysis of segmentation using 3DTRUS, CT, and MR. , 2007, International journal of radiation oncology, biology, physics.

[26]  Yang Lei,et al.  Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net. , 2019, Medical physics.

[27]  Santanu Chaudhury,et al.  Ultrasound Image Segmentation: A Deeply Supervised Network With Attention to Boundaries , 2019, IEEE Transactions on Biomedical Engineering.