Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks

By Ruida Cheng, Holger R. Roth, Nathan S. Lay, Le Lu, Baris Turkbey, W. Gandler, Evan S. McCreedy, T. Pohida, Peter A. Pinto, Peter L. Choyke and 2 more. 2017

66 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Pulmonary and Respiratory Medicine

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. P. Heng
2. Qi-Yan Dou
3. Lequan Yu
4. Quande Liu
5. Ronald M. Summers
6. Xiaosong Wang
7. Arvid Lundervold
8. Maryellen Lissak Giger

In Pulmonary and Respiratory Medicine

1. Qi-Yan Dou
2. Ronald M. Summers
3. Xiaosong Wang
4. P. Heng
5. Lequan Yu
6. Arvid Lundervold
7. Kenji Suzuki

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Cancer (PCa) [287] Proposed a method for end-to-end prostate segmentation by integrating holistically (image-to-image) nested”
• What the radiologist should know about artificial intelligence – an ESR white paper (2019, background): “Multiple studies have been published about abdominal (liver, pancreas, vessels) and pelvic (prostate) organ segmentation using a deep learning approach [45 – 51].”
• MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020, background): “A variety of deep-learning approaches [28]–[30] have achieved remarkable performance for automated prostate segmentation.”
• Deep learning in medical imaging and radiation therapy. (2018, methodology): “HNN has been successfully implemented in segmentation of the prostate 87 and brain tumors.”