Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

By Shijun Wang, Karen E. Burtt, Baris Turkbey, Peter L. Choyke, Ronald M. Summers. 2014

118 citations recorded by the source corpus.

Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

Subfields: Artificial Intelligence, 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 Artificial Intelligence

1. Qi-Yan Dou
2. P. Heng
3. Quande Liu
4. Guillaume Lemaitre
5. Amina Bolatkan
6. Robert Marti
7. Ben Glocker
8. Fabrice Mériaudeau

In Computer Vision and Pattern Recognition

1. P. Heng
2. Qi-Yan Dou
3. Quande Liu
4. S. Jinnai
5. Amina Bolatkan
6. Robert Marti
7. Ken Takasawa
8. Ben Glocker

In Pulmonary and Respiratory Medicine

1. Qi-Yan Dou
2. P. Heng

Citations in the corpus

• Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “Figure 1 shows a typical CADx workflow for detecting prostate cancer [44].”
• Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review (2015, background): “Only the recent work of [14] briefly covers a reduced number of current research works on CAD for CaP (a total of 70 references), but it does not provide a detailed description of any of the steps of a CAD system as it is done here with more than 200 references.”
• Unpaired Multi-Modal Segmentation via Knowledge Distillation (2020, background): “It has been widely applied for analyzing brain imaging [9], spinal structures [10], prostate cancer [11], and others.”