PSNet: prostate segmentation on MRI based on a convolutional neural network

By Zhiqiang Tian, Lizhi Liu, Zhenfeng Zhang, Baowei Fei. 2018

98 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Medical Imaging

How researchers see this paper

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

In Biomedical Engineering

1. Qi-Yan Dou
2. P. Heng
3. Lequan Yu
4. Hassan Elahi
5. Ronald M. Summers
6. Quande Liu
7. Meghavi Rana
8. Megha Bhushan

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. Hassan Elahi
8. Antonello Rizzi

In Medical Imaging

1. Xiaosong Wang
2. Ronald M. Summers
3. P. Heng
4. Qi-Yan Dou

Citations in the corpus

• Iterative Interaction Training for Segmentation Editing Networks (2018, neutral): “It has been shown that this architecture produces automatic segmentation results on medical images that is comparable to more complex architectures [16,21].”
• Deep learning in medical imaging and radiation therapy. (2018, background): “110 Samala et al. 199 studied the effects of fine-tuning different layers of the Alex-Net architecture and found that fine-tuning different layer combinations resulted in different performance.”
• Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, background): “Tian et al. used a fully convolutional network for prostate segmentation [165].”
• MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020, background): “Recently, more attention has been drawn to deep-learning-based methods [28]–[36] due to its automatic representation learning capability.”