Journal articles on the topic 'Lesions segmentation'
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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 textNoor, N. S. M., N. M. Saad, A. R. Abdullah, and N. M. Ali. "Automated segmentation and classification technique for brain stroke." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1832. http://dx.doi.org/10.11591/ijece.v9i3.pp1832-1841.
Full textde Oliveira, Marcela, Marina Piacenti-Silva, Fernando Coronetti Gomes da Rocha, Jorge Manuel Santos, Jaime dos Santos Cardoso, and Paulo Noronha Lisboa-Filho. "Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients." Diagnostics 12, no. 2 (January 18, 2022): 230. http://dx.doi.org/10.3390/diagnostics12020230.
Full textPitkänen, Johanna, Juha Koikkalainen, Tuomas Nieminen, Ivan Marinkovic, Sami Curtze, Gerli Sibolt, Hanna Jokinen, et al. "Evaluating severity of white matter lesions from computed tomography images with convolutional neural network." Neuroradiology 62, no. 10 (April 13, 2020): 1257–63. http://dx.doi.org/10.1007/s00234-020-02410-2.
Full textFourcade, Constance, Jean-Sebastien Frenel, Noémie Moreau, Gianmarco Santini, Aislinn Brennan, Caroline Rousseau, Marie Lacombe, et al. "PERCIST-like response assessment with FDG PET based on automatic segmentation of all lesions in metastatic breast cancer." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e13057-e13057. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e13057.
Full textFourcade, Constance, Jean-Sebastien Frenel, Noémie Moreau, Gianmarco Santini, Aislinn Brennan, Caroline Rousseau, Marie Lacombe, et al. "PERCIST-like response assessment with FDG PET based on automatic segmentation of all lesions in metastatic breast cancer." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e13057-e13057. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e13057.
Full textM D, Swetha, and Aditya C R. "Noise Invariant Convolution Neural Network for Segmentation of Multiple Sclerosis Lesions from Brain Magnetic Resonance Imaging." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 13 (October 19, 2022): 38–55. http://dx.doi.org/10.3991/ijoe.v18i13.34273.
Full textMeyer-Baese, A., T. Schlossbauer, O. Lange, and A. Wismueller. "Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI." International Journal of Biomedical Imaging 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/326924.
Full textLi, Yingjie, Chao Xu, Jubao Han, Ziheng An, Deyu Wang, Haichao Ma, and Chuanxu Liu. "MHAU-Net: Skin Lesion Segmentation Based on Multi-Scale Hybrid Residual Attention Network." Sensors 22, no. 22 (November 11, 2022): 8701. http://dx.doi.org/10.3390/s22228701.
Full textHojjatoleslami, S. A., and F. Kruggel. "Segmentation of large brain lesions." IEEE Transactions on Medical Imaging 20, no. 7 (July 2001): 666–69. http://dx.doi.org/10.1109/42.932750.
Full textHuang, Mingfeng, Guoqin Xu, Junyu Li, and Jianping Huang. "A Method for Segmenting Disease Lesions of Maize Leaves in Real Time Using Attention YOLACT++." Agriculture 11, no. 12 (December 2, 2021): 1216. http://dx.doi.org/10.3390/agriculture11121216.
Full textTang, Suigu, Xiaoyuan Yu, Chak-Fong Cheang, Zeming Hu, Tong Fang, I.-Cheong Choi, and Hon-Ho Yu. "Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model." Sensors 22, no. 4 (February 15, 2022): 1492. http://dx.doi.org/10.3390/s22041492.
Full textSwetha, R. "Multi-Lesion Segmentation of Diabetic Retinopathy Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2835–38. http://dx.doi.org/10.22214/ijraset.2022.44497.
Full textPang, Yachun, Li Li, Wenyong Hu, Yanxia Peng, Lizhi Liu, and Yuanzhi Shao. "Computerized Segmentation and Characterization of Breast Lesions in Dynamic Contrast-Enhanced MR Images Using Fuzzy c-Means Clustering and Snake Algorithm." Computational and Mathematical Methods in Medicine 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/634907.
Full textRajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue, and Sameer K. Antani. "A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs." Bioengineering 9, no. 9 (August 24, 2022): 413. http://dx.doi.org/10.3390/bioengineering9090413.
Full textFoo, Alex, Wynne Hsu, Mong Li Lee, Gilbert Lim, and Tien Yin Wong. "Multi-Task Learning for Diabetic Retinopathy Grading and Lesion Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (April 3, 2020): 13267–72. http://dx.doi.org/10.1609/aaai.v34i08.7035.
Full textZortea, Maciel, Stein Olav Skrøvseth, Thomas R. Schopf, Herbert M. Kirchesch, and Fred Godtliebsen. "Automatic Segmentation of Dermoscopic Images by Iterative Classification." International Journal of Biomedical Imaging 2011 (2011): 1–19. http://dx.doi.org/10.1155/2011/972648.
Full textLi, Yu, Meilong Zhu, Guangmin Sun, Jiayang Chen, Xiaorong Zhu, and Jinkui Yang. "Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy." Mathematical Biosciences and Engineering 19, no. 5 (2022): 5293–311. http://dx.doi.org/10.3934/mbe.2022248.
Full textZhang, Jinling, Jun Yang, and Min Zhao. "Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network." Scientific Programming 2021 (September 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/4614234.
Full textOkuboyejo, Damilola, and Oludayo O. Olugbara. "Segmentation of Melanocytic Lesion Images Using Gamma Correction with Clustering of Keypoint Descriptors." Diagnostics 11, no. 8 (July 29, 2021): 1366. http://dx.doi.org/10.3390/diagnostics11081366.
Full textJamil, Uzma, M. Usman Akram, Shehzad Khalid, Sarmad Abbas, and Kashif Saleem. "Computer Based Melanocytic and Nevus Image Enhancement and Segmentation." BioMed Research International 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/2082589.
Full textJaworek-Korjakowska, Joanna, and Pawel Kleczek. "Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation." Applied Sciences 8, no. 9 (August 22, 2018): 1430. http://dx.doi.org/10.3390/app8091430.
Full textLi, Dapeng, and Xiaoguang Liu. "Design of an Incremental Music Teaching and Assisted Therapy System Based on Artificial Intelligence Attention Mechanism." Occupational Therapy International 2022 (June 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/7117986.
Full textFuller, Sarah N., Ahmad Shafiei, David J. Venzon, David J. Liewehr, Michal Mauda Havanuk, Maran G. Ilanchezhian, Maureen Edgerly, et al. "Tumor Doubling Time Using CT Volumetric Segmentation in Metastatic Adrenocortical Carcinoma." Current Oncology 28, no. 6 (November 1, 2021): 4357–66. http://dx.doi.org/10.3390/curroncol28060370.
Full textJayachandran, A., and B. AnuSheeba. "Hybrid Melanoma Classification System Using Multi-Layer Fuzzy C-Means Clustering and Deep Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 11, no. 11 (November 1, 2021): 2709–15. http://dx.doi.org/10.1166/jmihi.2021.3873.
Full textJekel, Leon, Khaled Bousabarah, MingDe Lin, Sara Merkaj, Manpreet Kaur, Arman Avesta, Sanjay Aneja, et al. "NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET." Neuro-Oncology 24, Supplement_7 (November 1, 2022): vii162. http://dx.doi.org/10.1093/neuonc/noac209.622.
Full textLu, Fangfang, Chi Tang, Tianxiang Liu, Zhihao Zhang, and Leida Li. "Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images." Sensors 23, no. 5 (February 24, 2023): 2546. http://dx.doi.org/10.3390/s23052546.
Full textWang, Deli, Zheng Gong, Yanfen Zhang, and Shouxi Wang. "Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci." Contrast Media & Molecular Imaging 2021 (August 13, 2021): 1–9. http://dx.doi.org/10.1155/2021/2033806.
Full textDriessen, Julia, Gerben J. C. Zwezerijnen, Jakoba J. Eertink, Marie José Kersten, Anton Hagenbeek, Otto S. Hoekstra, Josée M. Zijlstra, and Ronald Boellaard. "Baseline Metabolic Tumor Volume in 18FDG-PET-CT Scans in Classical Hodgkin Lymphoma Using Semi-Automatic Segmentation." Blood 134, Supplement_1 (November 13, 2019): 4049. http://dx.doi.org/10.1182/blood-2019-125495.
Full textKalinovsky, A., V. Liauchuk, and A. Tarasau. "LESION DETECTION IN CT IMAGES USING DEEP LEARNING SEMANTIC SEGMENTATION TECHNIQUE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W4 (May 10, 2017): 13–17. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w4-13-2017.
Full textVélez, Paulina, Manuel Miranda, Carmen Serrano, and Begoña Acha. "Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?" Applied Sciences 12, no. 4 (February 17, 2022): 2092. http://dx.doi.org/10.3390/app12042092.
Full textFerrante, Matteo, Lisa Rinaldi, Francesca Botta, Xiaobin Hu, Andreas Dolp, Marta Minotti, Francesca De Piano, et al. "Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models." Journal of Clinical Medicine 11, no. 24 (December 9, 2022): 7334. http://dx.doi.org/10.3390/jcm11247334.
Full textTong, Xiaozhong, Junyu Wei, Bei Sun, Shaojing Su, Zhen Zuo, and Peng Wu. "ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation." Diagnostics 11, no. 3 (March 12, 2021): 501. http://dx.doi.org/10.3390/diagnostics11030501.
Full textMoreau, Noémie, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Aislinn Brennan, Ludovic Ferrer, Marie Lacombe, et al. "Automatic Segmentation of Metastatic Breast Cancer Lesions on 18F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment." Cancers 14, no. 1 (December 26, 2021): 101. http://dx.doi.org/10.3390/cancers14010101.
Full textHui, Haisheng, Xueying Zhang, Zelin Wu, and Fenlian Li. "Dual-Path Attention Compensation U-Net for Stroke Lesion Segmentation." Computational Intelligence and Neuroscience 2021 (August 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/7552185.
Full textHwang, Yoo Na, Min Ji Seo, and Sung Min Kim. "A Segmentation of Melanocytic Skin Lesions in Dermoscopic and Standard Images Using a Hybrid Two-Stage Approach." BioMed Research International 2021 (April 6, 2021): 1–19. http://dx.doi.org/10.1155/2021/5562801.
Full textAbdullah Hamad, Abdulsattar, Mustafa Musa Jaber, Mohammed Altaf Ahmed, Ghaida Muttashar Abdulsahib, Osamah Ibrahim Khalaf, and Zelalem Meraf. "Using Convolutional Neural Networks for Segmentation of Multiple Sclerosis Lesions in 3D Magnetic Resonance Imaging." Advances in Materials Science and Engineering 2022 (April 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/4905115.
Full textSatheesha, T. Y., D. Sathyanarayana, and M. N. Giri Prasad. "Proposed Threshold Algorithm for Accurate Segmentation for Skin Lesion." International Journal of Biomedical and Clinical Engineering 4, no. 2 (July 2015): 40–47. http://dx.doi.org/10.4018/ijbce.2015070104.
Full textDing, Xiangwen, and Shengsheng Wang. "Efficient Unet with depth-aware gated fusion for automatic skin lesion segmentation." Journal of Intelligent & Fuzzy Systems 40, no. 5 (April 22, 2021): 9963–75. http://dx.doi.org/10.3233/jifs-202566.
Full textGe, Ting, Ning Mu, Tianming Zhan, Zhi Chen, Wanrong Gao, and Shanxiang Mu. "Brain Lesion Segmentation Based on Joint Constraints of Low-Rank Representation and Sparse Representation." Computational Intelligence and Neuroscience 2019 (July 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/9378014.
Full textXie, Fei, Panpan Zhang, Tao Jiang, Jiao She, Xuemin Shen, Pengfei Xu, Wei Zhao, Gang Gao, and Ziyu Guan. "Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism." Electronics 10, no. 24 (December 13, 2021): 3103. http://dx.doi.org/10.3390/electronics10243103.
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