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When previous CT images are available for the same body region of the patient, the ability to obtain patient\u2010specific organ doses for CT \u2014 in a similar manner as radiation therapy treatment planning \u2014 will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep\u2010learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU\u2010based Monte Carlo rapid organ dose calculation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A deep convolutional neural network (CNN) based on the U\u2010Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas\u2010CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross\u2010validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU\u2010based Monte Carlo dose code, ARCHER, is used to calculate patient\u2010specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population\u2010average patient phantoms used in an off\u2010line dose reporting software, VirtualDose, at Massachusetts General Hospital.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population\u2010averaged phantoms, the new patient\u2010specific method achieved smaller absolute RDEs (mean\u00a0\u00b1\u00a0standard deviation) for all organs: 1.8%\u00a0\u00b1\u00a01.4% (vs 16.0%\u00a0\u00b1\u00a011.8%) for the lung, 0.8%\u00a0\u00b1\u00a00.7% (vs 34.0%\u00a0\u00b1\u00a031.1%) for the heart, 1.6%\u00a0\u00b1\u00a01.7% (vs 45.7%\u00a0\u00b1\u00a029.3%) for the esophagus, 0.6%\u00a0\u00b1\u00a01.2% (vs 15.8%\u00a0\u00b1\u00a012.7%) for the spleen, 1.2%\u00a0\u00b1\u00a01.0% (vs 18.1%\u00a0\u00b1\u00a015.7%) for the pancreas, 0.9%\u00a0\u00b1\u00a00.6% (vs 20.0%\u00a0\u00b1\u00a015.2%) for the left kidney, 1.7%\u00a0\u00b1\u00a03.1% (vs 19.1%\u00a0\u00b1\u00a09.8%) for the gallbladder, 0.3%\u00a0\u00b1\u00a00.3% (vs 24.2%\u00a0\u00b1\u00a018.7%) for the liver, and 1.6%\u00a0\u00b1\u00a01.7% (vs 19.3%\u00a0\u00b1\u00a013.6%) for the stomach. The trained automatic segmentation tool takes &lt;5\u00a0s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU\u2010accelerated ARCHER code take &lt;4\u00a0s per patient to achieve &lt;0.5% statistical uncertainty in all organ doses for all 103 patients in the database.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This work shows the feasibility to perform combined automatic patient\u2010specific multi\u2010organ segmentation of CT images and rapid GPU\u2010based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14131","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T23:20:09Z","timestamp":1583882409000},"page":"2526-2536","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["A method of rapid quantification of patient\u2010specific organ doses for CT using deep\u2010learning\u2010based multi\u2010organ segmentation and GPU\u2010accelerated Monte Carlo dose computing"],"prefix":"10.1002","volume":"47","author":[{"given":"Zhao","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingkun","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongming","family":"Shan","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Pei","sequence":"additional","affiliation":[{"name":"Department of Engineering and Applied Physics University of Science and Technology of China Hefei Anhui 230026 China"},{"name":"Anhui Wisdom Technology Company Limited Hefei Anhui 238000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Rensselaer Polytechnic Institute Troy NY 12180 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bob","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology Massachusetts General Hospital Boston MA 02114 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mannudeep K.","family":"Kalra","sequence":"additional","affiliation":[{"name":"Department of Radiology Massachusetts General Hospital Boston MA 02114 USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"X. 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