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Journal articles on the topic 'Autosegmentation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Chaney, Edward L., and Stephen M. Pizer. "Autosegmentation of Images in Radiation Oncology." Journal of the American College of Radiology 6, no. 6 (June 2009): 455–58. http://dx.doi.org/10.1016/j.jacr.2009.02.014.

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2

Savjani, Ricky. "nnU-Net: Further Automating Biomedical Image Autosegmentation." Radiology: Imaging Cancer 3, no. 1 (January 1, 2021): e209039. http://dx.doi.org/10.1148/rycan.2021209039.

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3

Liang, J., l. Zhuang, Y. Sun, H. Ye, and D. Yan. "Variation of Parotid Delineation Utilizing Autosegmentation Technique." International Journal of Radiation Oncology*Biology*Physics 87, no. 2 (October 2013): S700. http://dx.doi.org/10.1016/j.ijrobp.2013.06.1855.

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4

Claunch, C., A. Kanwar, B. Merz, S. Rana, A. Y. Hung, and R. F. Thompson. "Anatomical Edge Case Assessment for Prostate Cancer Autosegmentation." International Journal of Radiation Oncology*Biology*Physics 108, no. 3 (November 2020): e377-e378. http://dx.doi.org/10.1016/j.ijrobp.2020.07.2395.

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5

Shelley, L. E. A., M. P. F. Sutcliffe, K. Harrison, J. E. Scaife, M. A. Parker, M. Romanchikova, S. J. Thomas, R. Jena, and N. G. Burnet. "Autosegmentation of the rectum on megavoltage image guidance scans." Biomedical Physics & Engineering Express 5, no. 2 (January 10, 2019): 025006. http://dx.doi.org/10.1088/2057-1976/aaf1db.

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6

Cha, Elaine, Sharif Elguindi, Ifeanyirochukwu Onochie, Daniel Gorovets, Joseph O. Deasy, Michael Zelefsky, and Erin F. Gillespie. "Clinical implementation of deep learning contour autosegmentation for prostate radiotherapy." Radiotherapy and Oncology 159 (June 2021): 1–7. http://dx.doi.org/10.1016/j.radonc.2021.02.040.

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7

Chan, Jason W., Vasant Kearney, Samuel Haaf, Susan Wu, Madeleine Bogdanov, Nayha Dixit, Atchar Sudhyadhom, Josephine Chen, Sue S. Yom, and Timothy D. Solberg. "(OA40) Deep Learning-Based Autosegmentation for Head and Neck Radiotherapy." International Journal of Radiation Oncology*Biology*Physics 101, no. 2 (June 2018): e17. http://dx.doi.org/10.1016/j.ijrobp.2018.02.079.

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8

Kearney, V., J. Chan, M. Descovich, S. S. Yom, and T. D. Solberg. "A Multi-Task CNN Model for Autosegmentation of Prostate Patients." International Journal of Radiation Oncology*Biology*Physics 102, no. 3 (November 2018): S214. http://dx.doi.org/10.1016/j.ijrobp.2018.07.130.

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9

Geng, H., K. Men, N. Lee, P. Xia, and Y. Xiao. "Deep Learning Autosegmentation Model for NRG-HN001 Contour Quality Assurance." International Journal of Radiation Oncology*Biology*Physics 105, no. 1 (September 2019): E621—E622. http://dx.doi.org/10.1016/j.ijrobp.2019.06.1155.

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10

Bai, P., Z. Ling, Y. Sun, and J. Liang. "Evaluation of Atlas-based Autosegmentation for Head-and-Neck Cancer Patients." International Journal of Radiation Oncology*Biology*Physics 84, no. 3 (November 2012): S807. http://dx.doi.org/10.1016/j.ijrobp.2012.07.2160.

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11

Miskell, J., C. Thomas, and M. Pearson. "PO-1746: Deep learning artificial intelligence for autosegmentation of the bowel bag." Radiotherapy and Oncology 152 (November 2020): S969—S970. http://dx.doi.org/10.1016/s0167-8140(21)01764-3.

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12

Farrugia, Mark, Han Yu, Anurag K. Singh, and Harish Malhotra. "Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images." World Journal of Clinical Oncology 12, no. 2 (February 24, 2021): 95–102. http://dx.doi.org/10.5306/wjco.v12.i2.95.

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13

Caricato, P., S. Trivellato, E. Bonetto, V. Faccenda, D. Panizza, S. Arcangeli, and S. Meregalli. "PO-1334 Atlas Based Autosegmentation Of Organs At Risk In Gynaecological Cancer." Radiotherapy and Oncology 170 (May 2022): S1130—S1131. http://dx.doi.org/10.1016/s0167-8140(22)03298-4.

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14

Beasley, William J., Alan McWilliam, Adam Aitkenhead, Ranald I. Mackay, and Carl G. Rowbottom. "The suitability of common metrics for assessing parotid and larynx autosegmentation accuracy." Journal of Applied Clinical Medical Physics 17, no. 2 (March 2016): 41–49. http://dx.doi.org/10.1120/jacmp.v17i2.5889.

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15

Comsia, N. D., M. Quinn, K. Albuquerque, and J. C. Roeske. "Preliminary Evaluation of Cone Beam CT Autosegmentation in Patients with Cervical Cancer." International Journal of Radiation Oncology*Biology*Physics 78, no. 3 (November 2010): S413. http://dx.doi.org/10.1016/j.ijrobp.2010.07.972.

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16

Yang, Jinzhong, Harini Veeraraghavan, Samuel G. Armato, Keyvan Farahani, Justin S. Kirby, Jayashree Kalpathy-Kramer, Wouter van Elmpt, et al. "Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017." Medical Physics 45, no. 10 (September 19, 2018): 4568–81. http://dx.doi.org/10.1002/mp.13141.

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17

Liu, Hong, Haijun Wei, Lidui Wei, Jingming Li, and Zhiyuan Yang. "The Segmentation of Wear Particles Images UsingJ-Segmentation Algorithm." Advances in Tribology 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4931502.

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Abstract:

This study aims to use a JSEG algorithm to segment the wear particle’s image. Wear particles provide detailed information about the wear processes taking place between mechanical components. Autosegmentation of their images is key to intelligent classification system. This study examined whether this algorithm can be used in particles’ image segmentation. Different scales have been tested. Compared with traditional thresholding along with edge detector, the JSEG algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the JSEG method is suited for imaged wear particle segmentation and can be put into practical use in wear particle’s identification system.

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18

Heikkilä, J., T. Viren, H. Virsunen, K. Vuolukka, L. Voutilainen, R. Sawabi, H. Abouelazm, et al. "PD-0291: Comparison of different autosegmentation software for left-sided breast cancer patients." Radiotherapy and Oncology 152 (November 2020): S149—S150. http://dx.doi.org/10.1016/s0167-8140(21)00315-7.

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19

van den Berg, C. "SP-0699 Uncertainties and out-of-distribution estimation in deep learning-based autosegmentation." Radiotherapy and Oncology 170 (May 2022): S617. http://dx.doi.org/10.1016/s0167-8140(22)04020-8.

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20

Martin, S., N. Patil, S. Gaede, A. Louie, G. Bauman, D. D'Souza, T. Sexton, D. Palma, F. Khalvati, and G. Rodrigues. "A Multiphase Technological Validation of a MRI Prostate Cancer Computer Autosegmentation Software Algorithm." International Journal of Radiation Oncology*Biology*Physics 81, no. 2 (October 2011): S817. http://dx.doi.org/10.1016/j.ijrobp.2011.06.1438.

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21

Feng, Y., X. Ming, Y. Zhang, and P. Wang. "A Novel Autosegmentation Method for Lung Tumor on 18 F-FDG PET images." International Journal of Radiation Oncology*Biology*Physics 84, no. 3 (November 2012): S110. http://dx.doi.org/10.1016/j.ijrobp.2012.07.188.

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22

Marcus, David M., Eduard Schreibmann, Tim Fox, Karen D. Godette, Ian R. Crocker, and Peter J. Rossi. "Autosegmentation of the Prostate Gland for Post-Implant Dosimetry after Permanent Prostate Brachytherapy." Brachytherapy 12 (March 2013): S42—S43. http://dx.doi.org/10.1016/j.brachy.2013.01.078.

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23

Wang, Jiazhou, Jiayu Lu, Gan Qin, Lijun Shen, Yiqun Sun, Hongmei Ying, Zhen Zhang, and Weigang Hu. "Technical Note: A deep learning-based autosegmentation of rectal tumors in MR images." Medical Physics 45, no. 6 (May 3, 2018): 2560–64. http://dx.doi.org/10.1002/mp.12918.

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24

Petraikin, A. V., M. Ya Smoliarchuk, F. A. Petryaykin, L. A. Nisovtsova, Z. R. Artyukova, K. A. Sergunova, E. S. Akhmad, D. S. Semenov, A. V. Vladzymyrsky, and S. P. Morozov. "Assessment the Accuracy of Densitometry Measurements Using DMA PP2 Phantom." Traumatology and Orthopedics of Russia 25, no. 3 (October 18, 2019): 124–34. http://dx.doi.org/10.21823/2311-2905-2019-25-3-124-134.

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Abstract:

Purpose of the study — to assess the accuracy of dual energy x-ray absorptiometry (DXA ) for measurements of mineral bone density, bone mineral content, area of selected spine zone of examination as well as impact of subcutaneous fat layer and correction of auto-segmenting of the spine on the mentioned parameters. Material and Methods. The study was performed on iDXA scanner using the designed phantom DMA PP2 of the lumber spine with inlays to simulate subcutaneous fat (SF). To ensure correct assessment of measurements (precision and accuracy) the authors performed fivefold repeated scanning. Two modifications of the phantom were used, with and without SF inlays, as well as two methods for selection of spine range for examination – automatic and correction of autosegmentation. Results. Scanning of the phantom without SF inlays demonstrated a systematic understated values of bone mineral density (BMD) and bone mineral content (BMC) along the full measured interval: mean relative error of BMD for L1-L4 interval was 10.62% with automatic segmentation and 7.43% — with correction of autosegmentation. The least accuracy for BMD and BMC (1.53% and 0.90%, respectively) was observed during SF simulation and with correction of auto-segmentation of the spine. Analysis of variation coefficient for area of examined vertebrae, BMC and BMD demonstrated rather high precision of measurements, namely for BMD without SF in the L1-L4 interval amounted to 1.00% (auto-segmentation) and 0.56% (correction). Variation coefficient for scanning including SF inlays in the interval L1-L4 was 1.00% (auto-segmentation) and 0.68% (correction). Conclusion. The lowest level of accuracy was observed with the SFL object; in this case, the variation coefficient did not exceed 1% for all BMD interval. The mean value of the BMC accuracy also did not exceed 1% with the optimal scan parameters. The study proved the effectiveness of “RSK PK2” phantom when estimating the accuracy of BMD and BMC on iDXA scanner.

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25

Chen, Y. "SU-E-J-133: Autosegmentation of Linac CBCT: Improved Accuracy Via Penalized Likelihood Reconstruction." Medical Physics 42, no. 6Part9 (June 2015): 3295. http://dx.doi.org/10.1118/1.4924219.

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26

De Souza, K., R. Johnstone, C. Thomas, N. Milesi, T. Greener, D. Convery, T. Guerrero Urbano, and M. Lei. "EP-1130: Accuracy of atlas-based autosegmentation in head and neck radiotherapy treatment planning." Radiotherapy and Oncology 111 (2014): S29—S30. http://dx.doi.org/10.1016/s0167-8140(15)31248-2.

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27

Ayadi, M., R. Lynch, F. Lafay, and P. Pommier. "1294 poster PERFORMANCE EVALUATION OF THE ATLAS-BASED AUTOSEGMENTATION SOFTWARE (ABAS) FOR PROSTATE CANCER." Radiotherapy and Oncology 99 (May 2011): S483—S484. http://dx.doi.org/10.1016/s0167-8140(11)71416-5.

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28

Ten Kley, M., J. J. Penninkhof, M. Stoevelaar, S. Quint, B. J. M. Heijmen, and M. Hoogeman. "SP-0117: Clinical appplication of atlas-based autosegmentation for contouring of multiple treatment sites." Radiotherapy and Oncology 119 (April 2016): S53. http://dx.doi.org/10.1016/s0167-8140(16)31366-4.

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29

Wood, J., M. Aznar, and P. Whitehurst. "EP-1862 A comparative study of male pelvis CT autosegmentation and its clinical utility." Radiotherapy and Oncology 133 (April 2019): S1011—S1012. http://dx.doi.org/10.1016/s0167-8140(19)32282-0.

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30

Macomber, Meghan W., Mark Phillips, Ivan Tarapov, Rajesh Jena, Aditya Nori, David Carter, Loic Le Folgoc, Antonio Criminisi, and Matthew J. Nyflot. "Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features." Physics in Medicine & Biology 63, no. 23 (November 22, 2018): 235002. http://dx.doi.org/10.1088/1361-6560/aaeaa4.

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31

Harrison, A., J. Galvin, Y. Yu, and Y. Xiao. "SU-FF-J-172: Deformable Fusion and Atlas Based Autosegmentation: MimVista Vs. CMS Focal ABAS." Medical Physics 36, no. 6Part8 (June 2009): 2517. http://dx.doi.org/10.1118/1.3181465.

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32

Strolin, S., L. Strigari, V. Bruzzaniti, S. Ungania, R. Nigro, S. Riccardi, M. Casale, et al. "Evaluation of the autosegmentation tool of normal tissue structures in prostate cancer: A multicentric study." Physica Medica 32 (February 2016): 64–65. http://dx.doi.org/10.1016/j.ejmp.2016.01.222.

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33

Ibragimov, B., D. A. S. Toesca, D. T. Chang, A. C. Koong, and L. Xing. "Deep Learning-Based Autosegmentation of Portal Vein for Prediction of Central Liver Toxicity After SBRT." International Journal of Radiation Oncology*Biology*Physics 99, no. 2 (October 2017): E672. http://dx.doi.org/10.1016/j.ijrobp.2017.06.2221.

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34

Gillespie, Erin F., and Danielle Rodin. "Should We Contour Cardiac Substructures in Routine Practice? How Autosegmentation Helped Us Get There (or Not)." International Journal of Radiation Oncology*Biology*Physics 112, no. 3 (March 2022): 633–35. http://dx.doi.org/10.1016/j.ijrobp.2021.11.014.

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35

Xu, Huajian, Zhiwei Yang, Pengyuan He, Guisheng Liao, Min Tian, and Penghui Huang. "A Multifeature Autosegmentation-Based Approach for Inshore Ambiguity Identification and Suppression With Azimuth Multichannel SAR Systems." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 9 (September 2018): 3167–78. http://dx.doi.org/10.1109/jstars.2018.2857488.

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36

Hoggarth, M. A., M. Quinn, N. Comsia, K. Albuquerque, and J. C. Roeske. "Use of Autosegmentation Software to Contour Normal Tissues in Multi-fractional HDR Brachytherapy for Cervical Cancer." International Journal of Radiation Oncology*Biology*Physics 75, no. 3 (November 2009): S655. http://dx.doi.org/10.1016/j.ijrobp.2009.07.1496.

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37

Amjad, Asma, Jiaofeng Xu, Dan Thill, Colleen Lawton, William Hall, Musaddiq J. Awan, Monica Shukla, Beth A. Erickson, and X. Allen Li. "General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis." Medical Physics 49, no. 3 (February 7, 2022): 1686–700. http://dx.doi.org/10.1002/mp.15507.

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38

Moore, KL. "SU-FF-I-87: DTA-Based Metrics for the Evaluation of Autosegmentation Algorithms in Clinical Radiotherapy Workflow." Medical Physics 36, no. 6Part4 (June 2009): 2454. http://dx.doi.org/10.1118/1.3181207.

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39

Wikander, John, William L. Eisele, and David L. Schrank. "Speed-Based Autosegmentation Method for Reporting Moving Ahead for Progress in the 21st Century Act Performance Measures." Transportation Research Record: Journal of the Transportation Research Board 2460, no. 1 (January 2014): 164–75. http://dx.doi.org/10.3141/2460-18.

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40

Block, A. M., M. Quinn, N. D. Comsia, K. Albuquerque, and J. C. Roeske. "Dosimetric Evaluation of Autosegmentation Software to Contour Normal Tissues in Multi-fractional HDR Brachytherapy for Cervical Cancer." International Journal of Radiation Oncology*Biology*Physics 78, no. 3 (November 2010): S409—S410. http://dx.doi.org/10.1016/j.ijrobp.2010.07.964.

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41

Koo, Jihye, Jimmy J. Caudell, Kujtim Latifi, Petr Jordan, Sangyu Shen, Philip M. Adamson, Eduardo G. Moros, and Vladimir Feygelman. "Comparative evaluation of a prototype deep learning algorithm for autosegmentation of normal tissues in head and neck radiotherapy." Radiotherapy and Oncology 174 (September 2022): 52–58. http://dx.doi.org/10.1016/j.radonc.2022.06.024.

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42

Kanwar, A., C. Claunch, S. Rana, B. Merz, A. Y. Hung, and R. F. Thompson. "Evaluation of Commercial Pelvic Autosegmentation Solutions in Anatomic Edge Cases of Patients Receiving Definitive Radiotherapy for Prostate Cancer." International Journal of Radiation Oncology*Biology*Physics 111, no. 3 (November 2021): S43—S44. http://dx.doi.org/10.1016/j.ijrobp.2021.07.120.

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43

Chang, Yankui, Zhi Wang, Zhao Peng, Jieping Zhou, Yifei Pi, X. George Xu, and Xi Pei. "Clinical application and improvement of a CNN‐based autosegmentation model for clinical target volumes in cervical cancer radiotherapy." Journal of Applied Clinical Medical Physics 22, no. 11 (October 13, 2021): 115–25. http://dx.doi.org/10.1002/acm2.13440.

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44

Liang, Xiao, Howard Morgan, Dan Nguyen, and Steve Jiang. "Deep Learning–Based CT-to-CBCT Deformable Image Registration for Autosegmentation in Head and Neck Adaptive Radiation Therapy." Journal of Artificial Intelligence for Medical Sciences 2, no. 1-2 (2021): 62. http://dx.doi.org/10.2991/jaims.d.210527.001.

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45

Rivin del Campo, E., J. Cacicedo Fernandez de Bobadilla, C. Valentini, N. Andratschke, W. Mohamed, A. Abrunhosa-Branquinho, G. Chiloiro, et al. "PO-1532 The elephant in the room: teaching OAR delineation in a FALCON workshop in the autosegmentation era." Radiotherapy and Oncology 161 (August 2021): S1255—S1256. http://dx.doi.org/10.1016/s0167-8140(21)07983-4.

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46

Walker, G. V., M. Awan, R. Tao, E. J. Koay, G. B. Gunn, A. S. Garden, J. Phan, W. H. Morrison, D. I. Rosenthal, and C. D. Fuller. "Prospective Randomized Double-Blind Study of Atlas-Based Autosegmentation Assisted Radiation Treatment Planning in Head-and-Neck Cancer." International Journal of Radiation Oncology*Biology*Physics 87, no. 2 (October 2013): S619—S620. http://dx.doi.org/10.1016/j.ijrobp.2013.06.1639.

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47

Simmat, I., P. Georg, D. Georg, W. Birkfellner, G. Goldner, and M. Stock. "Assessment of accuracy and efficiency of atlas-based autosegmentation for prostate radiotherapy in a variety of clinical conditions." Strahlentherapie und Onkologie 188, no. 9 (June 7, 2012): 807–15. http://dx.doi.org/10.1007/s00066-012-0117-0.

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48

Comsia, N. D., M. A. Hoggarth, K. Albuquerque, S. Hernandez, F. Vali, and J. C. Roeske. "An Evaluation of Autosegmentation Software in Contouring Clinical Target Volume and Normal Tissue in Postoperative Endometrial Cancer Patients." International Journal of Radiation Oncology*Biology*Physics 75, no. 3 (November 2009): S369—S370. http://dx.doi.org/10.1016/j.ijrobp.2009.07.848.

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49

He, Fang, Rachel Ka Man Chun, Zicheng Qiu, Shijie Yu, Yun Shi, Chi Ho To, and Xiaojun Chen. "Choroid Segmentation of Retinal OCT Images Based on CNN Classifier and l 2 - l q Fitter." Computational and Mathematical Methods in Medicine 2021 (January 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/8882801.

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Abstract:

Optical coherence tomography (OCT) is a noninvasive cross-sectional imaging technology used to examine the retinal structure and pathology of the eye. Evaluating the thickness of the choroid using OCT images is of great interests for clinicians and researchers to monitor the choroidal thickness in many ocular diseases for diagnosis and management. However, manual segmentation and thickness profiling of choroid are time-consuming which lead to low efficiency in analyzing a large quantity of OCT images for swift treatment of patients. In this paper, an automatic segmentation approach based on convolutional neural network (CNN) classifier and l 2 - l q ( 0 < q < 1 ) fitter is presented to identify boundaries of the choroid and to generate thickness profile of the choroid from retinal OCT images. The method of detecting inner choroidal surface is motivated by its biological characteristics after light reflection, while the outer chorioscleral interface segmentation is transferred into a classification and fitting problem. The proposed method is tested in a data set of clinically obtained retinal OCT images with ground-truth marked by clinicians. Our numerical results demonstrate the effectiveness of the proposed approach to achieve stable and clinically accurate autosegmentation of the choroid.

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50

Lai, Chih-Ching, Hsin-Kai Wang, Fu-Nien Wang, Yu-Ching Peng, Tzu-Ping Lin, Hsu-Hsia Peng, and Shu-Huei Shen. "Autosegmentation of Prostate Zones and Cancer Regions from Biparametric Magnetic Resonance Images by Using Deep-Learning-Based Neural Networks." Sensors 21, no. 8 (April 12, 2021): 2709. http://dx.doi.org/10.3390/s21082709.

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Abstract:

The accuracy in diagnosing prostate cancer (PCa) has increased with the development of multiparametric magnetic resonance imaging (mpMRI). Biparametric magnetic resonance imaging (bpMRI) was found to have a diagnostic accuracy comparable to mpMRI in detecting PCa. However, prostate MRI assessment relies on human experts and specialized training with considerable inter-reader variability. Deep learning may be a more robust approach for prostate MRI assessment. Here we present a method for autosegmenting the prostate zone and cancer region by using SegNet, a deep convolution neural network (DCNN) model. We used PROSTATEx dataset to train the model and combined different sequences into three channels of a single image. For each subject, all slices that contained the transition zone (TZ), peripheral zone (PZ), and PCa region were selected. The datasets were produced using different combinations of images, including T2-weighted (T2W) images, diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) images. Among these groups, the T2W + DWI + ADC images exhibited the best performance with a dice similarity coefficient of 90.45% for the TZ, 70.04% for the PZ, and 52.73% for the PCa region. Image sequence analysis with a DCNN model has the potential to assist PCa diagnosis.

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