Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images

2017 ◽  
Author(s):  
Yohannes K. Tsehay ◽  
Nathan S. Lay ◽  
Holger R. Roth ◽  
Xiaosong Wang ◽  
Jin Tae Kwak ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Magnetic Resonance Images ◽  
Prostate Cancer Detection

scholarly journals Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Shota Ito ◽  
Yuichi Mine ◽  
Yuki Yoshimi ◽  
Saori Takeda ◽  
Akari Tanaka ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Temporomandibular Joint ◽  
Temporomandibular Disorders ◽  
Temporomandibular Disorder ◽  
Semantic Segmentation ◽  
Magnetic Resonance Images ◽  
Articular Disc

AbstractTemporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

scholarly journals Prostate cancer detection with magnetic resonance-ultrasound fusion biopsy: The role of systematic and targeted biopsies

Cancer ◽  
2016 ◽  
Vol 122 (6) ◽  
pp. 884-892 ◽  
Author(s):  
Christopher P. Filson ◽  
Shyam Natarajan ◽  
Daniel J.A. Margolis ◽  
Jiaoti Huang ◽  
Patricia Lieu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Cancer Detection ◽  
Fusion Biopsy ◽  
Prostate Cancer Detection ◽  
Targeted Biopsies
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