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7 papers · 2 filters
Echocardiography Segmentation with Enforced Temporal Consistency
Nathan Painchaud, Nicolas Duchateau, Olivier Bernard +1
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer…
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients
Verónica Muñoz-Ramírez, Nicolas Pinon, Florence Forbes +2
Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identif…
3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images
Sungmin Hong, Razvan Marinescu, Adrian V. Dalca +4
Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as,…
Bone Surface Reconstruction and Clinical Features Estimation from Sparse Landmarks and Statistical Shape Models: A feasibility study on the femur
Alireza Asvadi, Guillaume Dardenne, Jocelyne Troccaz +1
In this study, we investigated a method allowing the determination of the femur bone surface as well as its mechanical axis from some easy-to-identify bony landmarks. The reconstru…
EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation
Yuting He, Rongjun Ge, Xiaoming Qi +6
3D complete renal structures(CRS) segmentation targets on segmenting the kidneys, tumors, renal arteries and veins in one inference. Once successful, it will provide preoperative p…
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections
Fernando Pérez-García, Reuben Dorent, Michele Rizzi +9
Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-th…