Response monitoring of breast cancer on DCE-MRI using convolutional neural network-generated seed points and constrained volume growing
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Hugo J. Kuijf | Bas H. M. van der Velden | Kenneth G. A. Gilhuijs | Ivana Isgum | Bob D. de Vos | Claudette E. Loo | I. Išgum | K. Gilhuijs | H. Kuijf | C. Loo | B. D. Vos
[1] Max A. Viergever,et al. Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities , 2016, MICCAI.
[2] Guy Amit,et al. Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches , 2017, Medical Imaging.
[3] Ivan Dmitriev,et al. Association between Parenchymal Enhancement of the Contralateral Breast in Dynamic Contrast-enhanced MR Imaging and Outcome of Patients with Unilateral Invasive Breast Cancer. , 2015, Radiology.
[4] A. Seidman. Neoadjuvant Versus Adjuvant Systemic Treatment in Breast Cancer: A Meta-Analysis , 2006 .
[5] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[6] Bram van Ginneken,et al. A survey on deep learning in medical image analysis , 2017, Medical Image Anal..
[7] Brian B. Avants,et al. N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.
[8] Daniele Regge,et al. A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker , 2014, Comput. Methods Programs Biomed..
[9] D. Wickerham,et al. Effect of preoperative chemotherapy on local-regional disease in women with operable breast cancer: findings from National Surgical Adjuvant Breast and Bowel Project B-18. , 1997, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[10] M L Giger,et al. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. , 1998, Medical physics.
[11] Tim Leiner,et al. Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease , 2016, RAMBO+HVSMR@MICCAI.
[12] J. Peterse,et al. Breast MR imaging in women at increased lifetime risk of breast cancer: clinical system for computerized assessment of breast lesions initial results. , 2002, Radiology.
[13] S. Linn,et al. Neoadjuvant Therapy for Breast Cancer: Established Concepts and Emerging Strategies , 2017, Drugs.
[14] L. Esserman,et al. Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL. , 2012, Radiology.
[15] E. Rutgers,et al. A practical approach to manage additional lesions at preoperative breast MRI in patients eligible for breast conserving therapy: results , 2010, Breast Cancer Research and Treatment.
[16] Tanja Alderliesten,et al. Validation of Semiautomatic Measurement of the Extent of Breast Tumors Using Contrast-Enhanced Magnetic Resonance Imaging , 2007, Investigative radiology.
[17] Nico Karssemeijer,et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes , 2017, Medical physics.
[18] Nico Karssemeijer,et al. Automated localization of breast cancer in DCE-MRI , 2015, Medical Image Anal..
[19] J. A. van der Hage,et al. Neoadjuvant chemotherapy for operable breast cancer , 2007, The British journal of surgery.
[20] K. Gilhuijs,et al. Fully automated deformable registration of breast DCE-MRI and PET/CT , 2013, Physics in medicine and biology.
[21] S. Rodenhuis,et al. Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.