Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.
Ana Jiménez PastorRafael Lopez-GonzalezBelén Fos-GuarinosFabio Garcia-CastroMark WittenbergAsunción Torregrosa-AndrésLuis Marti-BonmatiMargarita Garcia-FontesPablo DuarteJuan Pablo GambiniLeonardo Kayat BittencourtFelipe Campos KitamuraVasantha Kumar VenugopalVidur MahajanPablo RosEmilio Soria-OlivasAngel Alberich-BayarriPublished in: European radiology (2023)
• Deep learning techniques allows the accurate segmentation of the prostate in three different regions on MR T2w images. • Multi-centric database proved the generalization of the CNN model on different institutions across different continents. • CNN models can be used to aid on the diagnosis and follow-up of patients with prostate cancer.
Keyphrases
- convolutional neural network
- deep learning
- prostate cancer
- magnetic resonance
- radical prostatectomy
- artificial intelligence
- machine learning
- contrast enhanced
- benign prostatic hyperplasia
- high resolution
- emergency department
- magnetic resonance imaging
- high throughput
- mass spectrometry
- optical coherence tomography
- adverse drug
- single cell