نتائج البحث - "Semi-automatic segmentation"

1

Accuracy of gross tumour volume delineation with [68Ga]-PSMA-PET compared to histopathology for high-risk prostate cancer

المصدر: Acta Oncologica. 63:503-510

مصطلحات موضوعية: histopathology, Prostate cancer, PSMA-PET, semi-automatic segmentation, Medicin och hälsovetenskap, Klinisk medicin, Radiologi och bildbehandling, Medical and Health Sciences, Clinical Medicine, Radiology, Nuclear Medicine and Medical Imaging

URL الوصول: https://lup.lub.lu.se/record/4102673a-6771-412c-b688-0cc8ed8ed40f
http://dx.doi.org/10.2340/1651-226X.2024.39041

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2

Academic Journal

Point cloud segmentation in heritage preservation. Advanced digital process for Historical Houses

المؤلفون: Romina Nespeca, Chiara Mariotti, Leonardo Petetta, Angela Mandriota

المساهمون: Nespeca, Romina, Mariotti, Chiara, Petetta, Leonardo, Mandriota, Angela

مصطلحات موضوعية: Architectural Heritage, Preservation, Point Cloud Processing, Semi-Automatic Segmentation, Data Mining

وصف الملف: ELETTRONICO

Relation: volume:48; issue:2; firstpage:325; lastpage:332; numberofpages:8; journal:THE INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES; https://hdl.handle.net/11566/326932; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85186953595; https://isprs-archives.copernicus.org/articles/XLVIII-2-W4-2024/325/2024/

الاتاحة: https://hdl.handle.net/11566/326932
https://doi.org/10.5194/isprs-archives-XLVIII-2-W4-2024-325-2024
https://isprs-archives.copernicus.org/articles/XLVIII-2-W4-2024/325/2024/

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3

Academic Journal

Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines.

المؤلفون: Kim, Sewon, Bae, Won C, Masuda, Koichi, Chung, Christine B, Hwang, Dosik

المصدر: Applied sciences (Basel, Switzerland). 8(9)

مصطلحات موضوعية: MR spine image, correlation, graph-based segmentation, semi-automatic segmentation, vertebral body, Bioengineering

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/8cn057wt

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4

Academic Journal

Semi-automatic muscle segmentation in MR images using deep registration-based label propagation

المؤلفون: Decaux, Nathan, Conze, Pierre-Henri, Ropars, Juliette, He, Xinyan, Sheehan, Frances, T, Pons, Christelle, Salem, Ben, Brochard, Sylvain, Rousseau, François

المساهمون: Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Institut Mines-Télécom Paris (IMT), National Institutes of Health Bethesda, MD, USA (NIH), Fondation ILDYS (ILDYS), ANR-19-CHIA-0015,AI-4-CHILD,IA au service de la neuroréhabilitation pédiatrique(2019)

المصدر: ISSN: 0031-3203 ; Pattern Recognition ; https://hal.science/hal-03945559 ; Pattern Recognition, 2023, 140 (August 2023), pp.109529. ⟨10.1016/j.patcog.2023.109529⟩.

مصطلحات موضوعية: semi-automatic segmentation, musculoskeletal system, deep registration, label propagation, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]

الاتاحة: https://hal.science/hal-03945559
https://hal.science/hal-03945559v2/document
https://hal.science/hal-03945559v2/file/PR___Few_shot_muscle_segmentation_in_MR_images_using_deep_registration_based_label_propagation%20%289%29.pdf
https://doi.org/10.1016/j.patcog.2023.109529

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5

Academic Journal

Automated analysis of lung lesions in COVID-19: comparison of standard and low-dose CT ; Автоматический анализ поражения легких при COVID-19: сравнение стандартной и низкодозной компьютерной томографии

المؤلفون: I. A. Blokhin, A. V. Solovev, A. V. Vladzymyrskyy, M. R. Kodenko, Yu. F. Shumskaya, A. P. Gonchar, V. A. Gombolevskiy, И. А. Блохин, А. В. Соловьев, A. B. Владзимирский, М. Р. Коденко, Ю. Ф. Шумская, А. П. Гончар, В. А. Гомболевский

المصدر: The Siberian Journal of Clinical and Experimental Medicine; Том 37, № 4 (2022); 114-123 ; Сибирский журнал клинической и экспериментальной медицины; Том 37, № 4 (2022); 114-123 ; 2713-265X ; 2713-2927

مصطلحات موضوعية: низкодозная компьютерная томография, COVID-19, thorax, semi-automatic segmentation, low-dose computed tomography, грудная клетка, полуавтоматическая сегментация

وصف الملف: application/pdf

Relation: https://www.sibjcem.ru/jour/article/view/1625/763; Lai C.-C., Shih T.-P., Ko W.-C., Tang H.-J., Hsueh P.-R. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int. J. Antimicrob. Agents. 2020;55(3):105924. DOI:10.1016/j.ijantimicag.2020.105924.; Вe Jaegere T.M.H., Krdzalic J., Fasen B.A.C.M., Kwee R.M.; COVID-19 CT Investigators South-East Netherlands (CISEN) study group. Radiological society of north america chest ct classification system for reporting COVID-19 pneumonia: Interobserver variability and correlation with reverse-transcription polymerase hain reaction. Radiol. Cardiothorac. Imaging. 2020;2(3):e200213. DOI:10.1148/ryct.2020200213.; Samir A., El-Husseiny R.M., Sweed R.A., El-Maaboud N.A.E.-M.A., Masoud M. Ultra-low-dose chest CT protocol during the second wave of COVID-19 pandemic: A double-observer prospective study on 250 patients to evaluate its detection accuracy. Egypt. J. Radiol. Nucl. Med. 2021;52(1):136. DOI:10.1186/s43055-021-00512-2.; Prokop M., van Everdingen W., van Rees Vellinga T., Quarles van Ufford H., Stöger L., Beenen L. et al. CO-RADS: A categorical СТ assessment scheme for patients suspected of having COVID-19-definition and evaluation. Radiol. 2020;296(2):E97–E104. DOI:10.1148/radiol.2020201473.; Yang R., Li X., Liu H., Zhen Y., Zhang X., Xiong Q. et al. Chest ct severity score: An imaging tool for assessing severe covid-19. Radiol. Cardiothorac. Imaging. 2020;2(2):e200047. DOI:10.1148/ryct.2020200047.; Colombi D., Bodini F.C., Petrini M., Maffi G., Morelli N., Milanese G. et al. Well-aerated lung on admitting chest CN to predict adverse outcome in COVID-19 pneumonia. Radiol. 2020;296(2):E86–E96. DOI:10.1148/radiol.2020201433.; Priority medical devices list for the COVID-19 response and associated technical specifications: Interim guidance. URL: https://apps.who.int/iris/bitstream/handle/10665/336745/WHO-2019-nCoV-MedDev-TS-O2T.V2-eng.pdf (22.11.2022).; Lee E.Y.P, Ng M.Y., Khong P.L. COVID-19 pneumonia: what has CT taught us? Lancet Infect. Dis. 2020;20(4):384–385. DOI:10.1016/S1473-3099(20)30134-1.; Xia T., Li J., Gao J., Xu X. Small solitary ground-glass nodule on СТ as an initial manifestation of coronavirus disease 2019 (COVID-19) pneumonia. Korean. J. Radiol. 2020;21(5):545. DOI:10.3348/kjr.2020.0240.; Li B., Li X., Wang Y., Han Y., Wang Y., Wang C. et al. Diagnostic value and key features of computed tomography in Coronavirus Disease 2019. Emerg. Microbes Infec. 2020;9(1):787–793. DOI:10.1080/22221751.2020.1750307.; Parekh M., Donuru A., Balasubramanya R., Kapur S. Review of the chest CT differential diagnosis of ground-glass opacities in the COVID era. Radiol. 2020;297(3):E289–E302. DOI:10.1148/radiol.2020202504.; Лучевая диагностика коронавирусной болезни (COVID-19): организация, методология, интерпретация результатов; 2 изд. URL: https://tele-med.ai/biblioteka-dokumentov/luchevaya-diagnostika-koronavirusnoj-bolezni-covid-19-organizaciya-metodologiya-interpretaciya-rezultatov2 (22.11.2022); Huang L., Han R., Ai T., Yu P., Kang H., Tao Q. et al. Serial quantitative chest CT assessment of COVID -19: A deep learning approach. Radiol: Cardiothorac. Imaging. 2020;2(2):e200075. DOI:10.1148/ ryct.2020200075.; Морозов С.П., Кузьмина Е.С., Ледихова Н.В., Владзимирский А.В., Трофименко И.А., Мокиенко О.А. и др. Мобилизация научно-практического потенциала службы лучевой диагностики г. Москвы в пандемию COVID-19. Digital Diagnostics. 2020;1(1):5−12. DOI:10.17816/DD51043.; Prasad K.N., Cole W.C., Haase G.M. Radiation protection in humans: Extending the concept of as low as reasonably achievable (Alara) from dose to biological damage. BJR. 2004;77(914):97–99. DOI:10.1259/bjr/88081058.; Preface, executive summary and glossary. Ann. ICRP. 2007;37(2–4):9– 34. DOI:10.1016/j.icrp.2007.10.003.; Sakane H., Ishida M., Shi L., Fukumoto W., Sakai C., Miyata Y. et al. Biological effects of low-dose chest CT on chromosomal DNA. Radiology. 2020;295(2):439–445. DOI:10.1148/radiol.2020190389.; Tofighi S., Najafi S., Johnston S.K., Gholamrezanezhad A. Low-dose CT in COVID-19 outbreak: Radiation safety, image wisely, and image gently pledge. Emerg. Radiol. 2020;27(6):601–605. DOI:10.1007/s10140-02001784-3.; Tabatabaei S.M.H, Talari H., Gholamrezanezhad A., Farhood B., Rahimi H., Razzaghi R. et al. A low-dose chest CT protocol for the diagnosis of COVID-19 pneumonia: A prospective study. Emerg. Radiol. 2020;27(6):607–615. DOI:10.1007/s10140-020-01838-6.; Schulze-Hagen M., Hübel C., Meier-Schroers M., Yüksel C., Sander A. et al. Low-dose chest CT for the diagnosis of COVID-19. Deutsches Ärzteblatt International. 2020;117(22–23):389–395. DOI:10.3238/arztebl.2020.0389.; Aslan S., Bekçi T., Çakır İ.M., Ekiz M., Yavuz İ., Şahin A.M. Diagnostic performance of low-dose chest CT to detect COVID-19: A Turkish population study. Diagn. Interv. Radiol. 2021;27(2):181–187. DOI:10.5152/dir.2020.20350.; Blokhin I., Gombolevskiy V., Chernina V., Gusev M., Gelezhe P., Aleshina O. et al. Inter-observer agreement between low-dose and standard-dose СТ with soft and sharp convolution kernels in СOVID-19 pneumonia. J. Clin. Med. 2022;11(3):669. DOI:10.3390/jcm11030669.; Усанов М.С., Кульберг Н.С., Морозов С.П. Разработка алгоритма анизотропной нелинейной фильтрации данных компьютерной томографии с применением динамического порога. Компьютерные исследования и моделирование. 2019;11(2):233–248. DOI:10.20537/2076-7633-2019-11-2-233-248.; Schilham A.M.R, van Ginneken B., Gietema H., Prokop M. Local noise weighted filtering for emphysema scoring of low-dose CT images. IEEE Trans. Med. Imaging. 2006;25(4):451–463. DOI:10.1109/TMI.2006.871545.; Николаев А.Е., Чернина В.Ю., Блохин И.А., Шапиев А.Н., Гончар А.П., Гомболевский В.А. и др. Перспективы использования комплексной компьютер-ассистированной диагностики в оценке структур грудной клетки. Хирургия. Журнал им. Н.И. Пирогова. 2019;(12):91–99. DOI:10.17116/hirurgia201912191.; Bai T., Wang B., Nguyen D., Jiang S. Probabilistic self‐learning framework for low‐dose CT denoising. Med. Phys. 2021;48(5):2258–2270. DOI:10.1002/mp.14796.; Tang C., Li J., Wang L., Li Z., Jiang L., Cai A. et al. Unpaired low-dose CT denoising network based on cycle-consistent generative adversarial network with prior image information. Comput. Math. Methods Med. 2019;2019:1–11. DOI:10.1155/2019/8639825.; Gombolevskiy V., Morozov S., Chernina V., Blokhin I., Vassileva J. A phantom study to optimise the automatic tube current modulation for chest CT in COVID-19. Eur. Radiol. Exp. 2021;5(1):21. DOI:10.1186/ s41747-021-00218-0.; Maldjian P.D., Goldman A.R. Reducing radiation dose in body СТ: primer on dose metrics and key CT technical parameters. Am. Jour. of Rent. 2013;200(4):741–747. DOI:10.2214/AJR.12.9768.; Gierada D.S., Bierhals A.J., Choong C.K., Bartel S.T., Ritter J.H., Das N.A. et al. Effects of CT section thickness and reconstruction kernel on emphysema quantification. Acad. Radiol. 2010;17(2):146–156. DOI:10.1016/j.acra.2009.08.007.; Fedorov A., Beichel R., Kalpathy-Cramer J., Finet J., Fillion-Robin J.-C., Pujol S. et al. 3D slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging. 2012;30(9):1323–1341. DOI:10.1016/j.mri.2012.05.001.; Kikinis R., Pieper S.D., Vosburgh K.G. 3D slicer: F platform for subject-specific image analysis, visualization, and clinical support. In: F.A. Jolesz by ed. Intraoperative imaging andiImage-guided therapy. New York: Springer; 2014:277–289. DOI:10.1007/978-1-4614-76573_19.; Bumm R., Lasso A., Kawel-Böhm N., Wäckerlin A., Ludwig P., Furrer M. First results of spatial reconstruction and quantification of COVID-19 chest CT infiltrates using lung CT analyzer and 3D slicer. Brit. J. Surg. 2021;108(4):znab202.077. DOI:10.1093/bjs/znab202.077.; Kaza E., Dunlop A., Panek R., Collins D.J., Orton M., Symonds-Tayler R. et al. Lung volume reproducibility under ABC control and self-sustained breath-holding. J. Appl. Clin. Med. Phys. 2017;18(2):154–162. DOI:10.1002/acm2.12034.; Lanza E., Muglia R., Bolengo I., Santonocito O.G., Lisi C., Angelotti G. et al. Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation. Eur. Radiol. 2020;30(12):6770– 6778. DOI:10.1007/s00330-020-07013-2.; Berta L., Rizzetto F., De Mattia C., Lizio D., Felisi M., Colombo P.E. et al. Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis. Phys. Medica. 2021;87:115–122. DOI:10.1016/j.ejmp.2021.06.001.; Ozsahin I., Sekeroglu B., Musa M.S., Mustapha M.T., Uzun Ozsahin D. Review on diagnosis of covid-19 from chest CT images using artificial intelligence. Comput. Math. Methods Med. 2020;2020:1–10. DOI:10.1155/2020/9756518.; Shi F., Wang J., Shi J., Wu Z., Wang Q., Tang Z. et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. 2021;14:4–15. DOI:10.1109/RBME.2020.2987975.; Кульберг Н.С., Решетников Р.В., Новик В.П., Елизаров А.Б., Гусев М.А., Гомболевский В.А. и др. Вариабельность заключений при интерпретации КТ-снимков: один за всех и все за одного. Digital Diagnostics. 2021;2(2):105–118. DOI:10.17816/DD60622.; Boufarasse Y.B., Ettahir A., Bekkali D., Bennani J. Teleradiology and AI as solution to overcome the COVID-19 pandemic impact during the lockdowns in Africa. Health Sci. J. 2020;14(6):771. DOI:10.36648/1791809X.14.6.771.; Tan B.S., Dunnick N.R., Gangi A., Goergen S., Jin Z.Y., Neri E. et al. RSNA International Trends: A global perspective on the COVID-19 pandemic and radiology in late 2020. Radiol. 2021;299(1):E193–E203. DOI:10.1148/radiol.2020204267.; Martín-Noguerol T., Lopez-Ortega R., Ros P.R., Luna A. Teleworking beyond teleradiology: Managing radiology departments during the COVID-19 outbreak. Eur. Radiol. 2021;31(2):601–604. DOI:10.1007/s00330-020-07205-w.; https://www.sibjcem.ru/jour/article/view/1625

الاتاحة: https://www.sibjcem.ru/jour/article/view/1625
https://doi.org/10.29001/2073-8552-2022-37-4-114-123

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6

Academic Journal

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation

المؤلفون: Yaojiang Ye, Zixin Luo, Zhengxuan Qiu, Kangyang Cao, Bingsheng Huang, Lei Deng, Weijing Zhang, Guoqing Liu, Yujian Zou, Jian Zhang, Jianpeng Li

المصدر: Bioengineering, Vol 10, Iss 12, p 1355 (2023)

مصطلحات موضوعية: magnetic resonance imaging, urinary bladder neoplasms, radiomics, muscles, semi-automatic segmentation, Technology, Biology (General), QH301-705.5

Relation: https://www.mdpi.com/2306-5354/10/12/1355; https://doaj.org/toc/2306-5354; https://doaj.org/article/6cbe03f1620c412ca07cd38a8441b8c4

الاتاحة: https://doi.org/10.3390/bioengineering10121355
https://doaj.org/article/6cbe03f1620c412ca07cd38a8441b8c4

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7

Academic Journal

Assessment of immunotherapy response in intracranial malignancy using semi-automatic segmentation on magnetic resonance images

المؤلفون: Jia Tan, Chang Liu, Yan Li, Yiqi Ma, Ruoxi Xie, Zheng Li, Hengjiang Wan, Su Lui, Min Wu

المصدر: Frontiers in Immunology, Vol 13 (2022)

مصطلحات موضوعية: immunotherapy response, RECIST1.1, intracranial malignancy, magnetic resonance imaging, semi-automatic segmentation, Immunologic diseases. Allergy, RC581-607

وصف الملف: electronic resource

Relation: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1029656/full; https://doaj.org/toc/1664-3224

URL الوصول: https://doaj.org/article/d268d3e481eb4d14a124ce6bd64c8fc0

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8

Academic Journal

Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

المؤلفون: Virginia Liberini, Bruno De Santi, Osvaldo Rampado, Elena Gallio, Beatrice Dionisi, Francesco Ceci, Giulia Polverari, Philippe Thuillier, Filippo Molinari, Désirée Deandreis

المصدر: EJNMMI Physics, Vol 8, Iss 1, Pp 1-21 (2021)

مصطلحات موضوعية: Texture analysis, Radiomics, Neuroendocrine tumor, Robustness, 68Ga-DOTATOC PET/CT, Semi-automatic segmentation, Medical physics. Medical radiology. Nuclear medicine, R895-920

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2197-7364

URL الوصول: https://doaj.org/article/0a88e43c01754edf801f3efa0e7c295c

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9

Conference

Preliminary assessment of semiautomated segmentation tool for GBM neurosurgery: a BraTS-Toolkit approach

المؤلفون: Valeria Cerina, Chiara B. Rui, Elisabetta De Bernardi, Rosa M. Moresco, Carlo G. Giussani, Gianpaolo Basso, Andrea Di Cristofori

المساهمون: Cerina, V, Rui, C, DE BERNARDI, E, Moresco, R, Giussani, C, Basso, G, DI CRISTOFORI, A

مصطلحات موضوعية: Glioblastoma (GBM), Semi-automatic segmentation, GBM surgery planning, Segmentation quality assessment, BraTS Toolkit

Relation: 19th European Molecular Imaging Meeting - EMIM (European Society of Molecular Imaging - ESMI) - 12-15 March; firstpage:76; lastpage:76; numberofpages:1; https://hdl.handle.net/10281/468482; https://e-smi.eu/meetings/emim/past-meetings/2024-porto/

الاتاحة: https://hdl.handle.net/10281/468482
https://e-smi.eu/meetings/emim/past-meetings/2024-porto/

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