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Fully Automatic Catheter Segmentation in MRI with 3D Convolutional Neural Networks: Application to MRI-guided Gynecologic Brachytherapy

Institution:
1Department of Experimental and Clinical Medicine, Universita degli Studi Magna Graecia di Catanzaro, Viale Europa, campus Universitario S. Venuta, lab biomeccatronica, Catanzaro, Italy.
2None, France.
3University of Lubeck, Luebeck, Germany.
4Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
5Oncology department, Hebei Provincial General Hospital, Shijiazhuang, Hebei, China.
Publication Date:
Aug-2019
Journal:
Phys Med Biol
Volume Number:
64
Issue Number:
16
Pages:
165008
Citation:
Phys Med Biol. 2019 Aug 14;64(16):165008.
PubMed ID:
31272095
Keywords:
Brachytherapy, Catheter segmentation, Convolutional Neural Networks, Gynecologic cancer, Image guided surgery, Medical image segmentation
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Zaffino P., Pernelle G., Mastmeyer A., Mehrtash A., Zhang H., Kikinis R., Kapur T., Spadea M.F. Fully Automatic Catheter Segmentation in MRI with 3D Convolutional Neural Networks: Application to MRI-guided Gynecologic Brachytherapy. Phys Med Biol. 2019 Aug 14;64(16):165008. PMID: 31272095.
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External-beam radiotherapy followed by High Dose Rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by Magnetic Resonance Imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to Computed Tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3-dimensional Convolutional Neural Network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the 5 networks and the final segmentation was generated by voting on the obtained 5 candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0±3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60±0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.