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Curved planar reformatting and convolutional neural network‐based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI

机译：弯曲平面重新系重新系列小肠的卷积神经网络分割，从MRI进行可视化和定量评估儿科克罗恩病的定量评估

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Yechiel Lamash; Sila Kurugol; Moti Freiman; Jeannette M. Perez‐Rossello; Michael J. Callahan; Athos Bousvaros; Simon K. Warfield;
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年度 2018
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1. Curved planar reformatting and convolutional neural network‐based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI [J] . Lamash Yechiel, Kurugol Sila, Freiman Moti, Journal of magnetic resonance imaging: JMRI . 2019,第6期

机译：弯曲平面重新系列和卷积神经网络的小肠，以便于MRI对儿科克罗恩病的可视化和定量评估
2. Multi-planar 3D breast segmentation in MRI via deep convolutional neural networks [J] . Artificial intelligence in medicine . 2020,第Mara期

机译：深度卷积神经网络在MRI中进行多平面3D乳房分割
3. Dynamic MRI of the small bowel: usefulness of quantitative contrast-enhancement parameters and time-signal intensity curves for differentiating between active and inactive Crohn's disease. [J] . Giusti S, Faggioni L, Neri E, Abdominal imaging. . 2010,第6期

机译：小肠的动态MRI：定量对比增强参数和时间信号强度曲线对区分活动性和非活动性克罗恩病的有用性。
4. Automated Segmentation and Radiomic Characterization of Visceral Fat on Bowel MRIs for Crohn's Disease [C] . Iulia Barbur, Jacob Kurowski, Kaustav Bera, SPIE Medical Imaging Conference . 2018

机译：肠MRI对克罗恩病的内脏脂肪的自动分割和放射学表征
5. Automated Segmentation and Uncertainty Quantification for Image-Based Cardiovascular Modeling with Convolutional Neural Networks [D] . Maher, Gabriel. 2020

机译：卷积神经网络的图像基础心血管建模的自动分割和不确定量化
6. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI [O] . Han Jiao, Xinhua Jiang, Zhiyong Pang, 2020

机译：DCE-MRI中基于深度卷积神经网络的乳房自动分割和质量检测
7. Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN) [O] . Pradipta Kumar Mishra, Suresh Chandra Satapathy, Minakhi Rout 2021

机译：利用基于优化的深卷积神经网络（DCNN）分割MRI脑肿瘤图像

Curved planar reformatting and convolutional neural network‐based segmentation of the small bowel for visualization and quantitative assessment of pediatric Crohn's disease from MRI

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