Academic literature on the topic 'Lesions segmentation'
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Journal articles on the topic "Lesions segmentation"
Ma, Tian, Xinlei Zhou, Jiayi Yang, Boyang Meng, Jiali Qian, Jiehui Zhang, and Gang Ge. "Dental Lesion Segmentation Using an Improved ICNet Network with Attention." Micromachines 13, no. 11 (November 7, 2022): 1920. http://dx.doi.org/10.3390/mi13111920.
Full textVerma, Khushboo, Satwant Kumar, and David Paydarfar. "Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images." Diagnostics 12, no. 9 (August 24, 2022): 2055. http://dx.doi.org/10.3390/diagnostics12092055.
Full textRossi, Farli. "APPLICATION OF A SEMI-AUTOMATED TECHNIQUE IN LUNG LESION SEGMENTATION." Jurnal Teknoinfo 15, no. 1 (January 15, 2021): 56. http://dx.doi.org/10.33365/jti.v15i1.945.
Full textAbdullah, Bassem A., Akmal A. Younis, and Nigel M. John. "Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs." Open Biomedical Engineering Journal 6, no. 1 (May 9, 2012): 56–72. http://dx.doi.org/10.2174/1874120701206010056.
Full textWang, Xueling, Xianmin Meng, and Shu Yan. "Deep Learning-Based Image Segmentation of Cone-Beam Computed Tomography Images for Oral Lesion Detection." Journal of Healthcare Engineering 2021 (September 21, 2021): 1–7. http://dx.doi.org/10.1155/2021/4603475.
Full textXiong, Hui, Laith R. Sultan, Theodore W. Cary, Susan M. Schultz, Ghizlane Bouzghar, and Chandra M. Sehgal. "The diagnostic performance of leak-plugging automated segmentation versus manual tracing of breast lesions on ultrasound images." Ultrasound 25, no. 2 (January 25, 2017): 98–106. http://dx.doi.org/10.1177/1742271x17690425.
Full textWang, Ying, Jie Su, Qiuyu Xu, and Yixin Zhong. "A Collaborative Learning Model for Skin Lesion Segmentation and Classification." Diagnostics 13, no. 5 (February 28, 2023): 912. http://dx.doi.org/10.3390/diagnostics13050912.
Full textLiang, Yingbo, and Jian Fu. "Watershed Algorithm for Medical Image Segmentation Based on Morphology and Total Variation Model." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1954019. http://dx.doi.org/10.1142/s0218001419540193.
Full textKaur, Manpreet, Sunitha Varghese, Leon Jekel, Niklas Tillmanns, Sara Merkaj, Khaled Bousabarah, MingDe Lin, Jitendra Bhawnani, Veronica Chiang, and Mariam Aboian. "NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERY." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii162—vii163. http://dx.doi.org/10.1093/neuonc/noac209.626.
Full textMechrez, Roey, Jacob Goldberger, and Hayit Greenspan. "Patch-Based Segmentation with Spatial Consistency: Application to MS Lesions in Brain MRI." International Journal of Biomedical Imaging 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/7952541.
Full textDissertations / Theses on the topic "Lesions segmentation"
Abdullah, Bassem A. "Segmentation of Multiple Sclerosis Lesions in Brain MRI." Scholarly Repository, 2012. http://scholarlyrepository.miami.edu/oa_dissertations/711.
Full textNaeslund, Elin. "Stroke Lesion Segmentation for tDCS." Thesis, Linköpings universitet, Medicinsk informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71472.
Full textWan, Fengkai. "Deep Learning Method used in Skin Lesions Segmentation and Classification." Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.
Full textCabezas, Grebol Mariano. "Atlas-based segmentation of multiple sclerosis lesions in magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/119608.
Full textAquesta tesi es centra en la segmentació de imatges de ressonància magnètica del cervell aplicada a pacients d'esclerosi múltiple. Aquesta malaltia es caracteritza per l'aparició de lesions de matèria blanca, visibles en aquesta modalitat d'imatge. Després d'un anàlisi exhaustiu de l'estat de l'art en aquest tòpic, remarcant la importància de la informació prèvia, i també de la segmentació basada en atles del cervell, proposem dues estratègies diferents per a la segmentació de lesions basades en les conclusions d'ambdós estudis. La primera proporciona una classificació inicial dels teixits mitjançant una extensió de l'algorisme d'esperança-maximització, que es refina posteriorment amb un procés de segmentació de les lesions basat en una binarització inicial i una conseqüent estratègia de reducció de falsos positius a nivell de regió. La segona proposta es focalitza bàsicament en la segmentació de lesions i utilitza una combinació de classificadors febles entrenats amb un ric conjunt de característiques que inclou imatges d'intensitat, mapes probabilístics provinents d'un atles, un mapa d'intensitats atípiques i informació contextual. Ambdues estratègies han estat provades amb una nova base de dades formada per imatges de tres hospitals diferents amb diferent càrrega lesional per cas. L'avaluació d'aquestes proves, que s'ha dut a terme de forma quantitativa i qualitativa, inclou una comparativa i utilitza diferents mètriques de detecció i segmentació. L'anàlisi d'aquests resultats apunta a un millor rendiment relatiu a l'estat de l'art actual, amb una millor detecció per part de la primera estratègia i una millor segmentació per part de la segona
Ma, Pu. "Automatic segmentation of multiple sclerosis lesions in magnetic resonance brain images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63536.pdf.
Full textGarcía-Lorenzo, Daniel. "Robust Segmentation of Focal Lesions on Multi-Sequence MRI in Multiple Sclerosis." Phd thesis, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00485645.
Full textGarcía, Lorenzo Daniel. "Robust segmentation of focal lesions on multi-sequence MRI in multiple sclerosis." Rennes 1, 2010. http://www.theses.fr/2010REN1S018.
Full textMultiple sclerosis (MS) affects around 80. 000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic workflow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our workflow using synthetic and clinical images of two different centers to verify its accuracy and robustness
ASLANI, SHAHAB. "Deep learning approaches for segmentation of multiple sclerosis lesions on brain MRI." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997626.
Full textPeruch, Francesco. "(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424252.
Full textIl Melanoma è una forma molto aggressiva di cancro alla pelle la cui incidenza è costantemente aumentata negli ultimi 50 anni. Una diagnosi precoce unita ad una rapida asportazione risulta indispensabile per migliorare il tasso di sopravvivenza e richiede una analisi periodica ed accurata della lesioni melanocitiche del paziente. Abbiamo sviluppato una soluzione hardware e software chiamata Mole Mapper per assistere i deramtologi durante l'intero processo di diagnosi. L'obiettivo è permettere un incremento dell'accuratezza della diagnosi velocizzando al contempo l'intero processo. Tali caratteristiche si sono ottenute grazie ad un'analisi automatica delle immagini dermatoscopiche che individua ed evidenza al dermatologo le informazioni più significative. In questa tesi presentiamo 3 principali algoritmi che sono stati implementati in Mole Mapper: Una robusta segmentazione di lesioni melanocitiche, che risulta il punto di partenza di ogni altro algoritmo di elaborazioni di immagini e permette l'estrazione di informazioni utili riguardanti la forma e la dimensione delle lesioni. Tale algoritmo supera in accuratezza e velocità lo stato dell'arte attuale, con una precisione paragonabile ad un dermatologo esperto ed un tempo di esecuzione compatibile con l'elaborazione video realtime; Un algoritmo di depilazione digitale, che garantisce miglior precisione e robustezza agli altri algoritmi di elaborazione di immagini a fornisce al dermatologo un immagine priva di peli da impiegare nel processo di valutazione. La nostra proposta supera l'accuratezza dello stato dell'arte richiedendo solo una frazione del tempo di esecuzione, tanto da poter essere integrata su dispositivi mobili all'interno di una GUI interattiva. Un algoritmo di registrazione, per studiare l'evoluzione delle lesioni nel tempo evidenziando ogni possibile anomalia o variazione. Data la mancanza di un approccio standard al problema, abbiamo caratteriizzato gli obbiettivi ed i vincoli a cui sottostare proponendo quindi un approccio con un tempo di esecuzione compatibile con le necessità del Mole Mapper ed un accuratezza paragonabile a quella di un operatore umano.
Cui, Shenshen. "Fully Automatic Segmentation of White Matter Lesions from Multispectral Magnetic Resonance Imaging Data." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122650.
Full textA fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1,T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter,grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuro radiologists.
Key Words: White matter lesion, automatic segmentation, graph cuts, MRI, PIVUS
Book chapters on the topic "Lesions segmentation"
Kruggel, Frithjof. "Segmentation of Focal Brain Lesions." In Lecture Notes in Computer Science, 10–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28626-4_2.
Full textRodríguez, Roberto, and Oriana Pacheco. "A Strategy for Atherosclerotic Lesions Segmentation." In Lecture Notes in Computer Science, 171–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11578079_19.
Full textSharma, Rakesh, Jasjit S. Suri, and Ponnada A. Narayana. "Segmentation Techniques in the Quantification of Multiple Sclerosis Lesions in MRI." In Advanced Algorithmic Approaches to Medical Image Segmentation, 318–40. London: Springer London, 2002. http://dx.doi.org/10.1007/978-0-85729-333-6_5.
Full textMa, Zhen, and João Manuel R. S. Tavares. "Segmentation of Skin Lesions Using Level Set Method." In Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, 228–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09994-1_20.
Full textZeng, Ziming, and Reyer Zwiggelaar. "Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions." In Computer Vision/Computer Graphics Collaboration Techniques, 133–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24136-9_12.
Full textShahar, Allon, and Hayit Greenspan. "Probabilistic Spatial-Temporal Segmentation of Multiple Sclerosis Lesions." In Lecture Notes in Computer Science, 269–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27816-0_23.
Full textNageswaran, Sharmila, S. Vidhya, and Deepa Madathil. "Segmentation and Clinical Outcome Prediction in Brain Lesions." In Machine Learning and IoT, 181–91. Boca Raton : Taylor & Francis, 2019.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351029940-11.
Full textFranco, M. L. N., L. M. Nunes, A. P. P. Froner, A. M. M. Silva, and A. C. Patrocinio. "Influence of ROI pattern on segmentation in lung lesions." In IFMBE Proceedings, 211–14. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_51.
Full textKubicek, Jan, Iveta Bryjova, Marek Penhaker, Jana Javurkova, and Lukas Kolarcik. "Segmentation of Macular Lesions Using Active Shape Contour Method." In Advances in Intelligent Systems and Computing, 213–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27644-1_20.
Full textAdegun, Adekanmi, and Serestina Viriri. "An Enhanced Deep Learning Framework for Skin Lesions Segmentation." In Computational Collective Intelligence, 414–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28377-3_34.
Full textConference papers on the topic "Lesions segmentation"
Ibarrola Chamorro, Claudia Raquel, and Wagner Coelho de Albuquerque Pereira. "Segmentation of Mammary Lesions in Ultrasound Images Applying Mask R-CNN." In Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/latinoware.2019.10352.
Full textWen, AiQing, Xiaochuan Chen, Anni Chen, Hongyu Shi, and Yuming Hong. "Segmentation of kidney lesions with attention model based on Deeplab." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.032.
Full textRamli, Roshaslinie, Aamir Saeed Malik, Ahmad Fadzil M. Hani, and Felix Boon-Bin Yap. "Segmentation of Acne Vulgaris Lesions." In 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2011. http://dx.doi.org/10.1109/dicta.2011.63.
Full textNing, Yang, Chenbo Shi, Li Wang, and Chang Shu. "Automatic segmentation of psoriasis lesions." In SPIE/COS Photonics Asia, edited by Qionghai Dai and Tsutomu Shimura. SPIE, 2014. http://dx.doi.org/10.1117/12.2074372.
Full textVesal, Sulaiman, Nishant Ravikumar, and Andreas K. Maier. "KidNet: An Automated Framework for Renal Lesions Detection and Segmentation in CT Images." In 2019 Kidney Tumor Segmentation Challenge: KiTS19. University of Minnesota Libraries Publishing, 2019. http://dx.doi.org/10.24926/548719.051.
Full textPeris-Fajarnés, Guillermo, María Moncho Santonja, María Begoña Sanz Alamán, Beatriz Defez García, and Ismael Lengua Lengua. "Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images." In INNODOCT 2019. Valencia: Universitat Politècnica de València, 2019. http://dx.doi.org/10.4995/inn2019.2019.10946.
Full textJazzar, Nesrine, and Ali Douik. "A New Deep-Net Architecture for Ischemic Stroke Lesion Segmentation." In 4th International Conference on Machine Learning & Applications (CMLA 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121108.
Full textMasood, N. A., H. M. Mashali, and Abdalla S. A. Mohamed. "Color Segmentation for Skin Lesions Classification." In 2008 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2008. http://dx.doi.org/10.1109/cibec.2008.4786059.
Full textKeceli, Ali Seydi, and Ahmet Burak Can. "Automatic segmentation of white matter lesions." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136362.
Full textSáez, Aurora, Begoña Acha, and Carmen Serrano. "Segmentation and classification of dermatological lesions." In SPIE Medical Imaging, edited by Nico Karssemeijer and Ronald M. Summers. SPIE, 2010. http://dx.doi.org/10.1117/12.844323.
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