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Found 9 papers, 0 papers with code
CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions
This research embarked on a comparative exploration of the holistic segmentation capabilities of Convolutional Neural Networks (CNNs) in both 2D and 3D formats, focusing on cystic fibrosis (CF) lesions.
Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos
To this end, a computer-aided video classifier was developed to predict in-situ the morphology of stone using an intra-operative digital endoscopic video acquired in a clinical setting.
Towards Automatic Recognition of Pure & Mixed Stones using Intraoperative Endoscopic Digital Images
A deep convolutional neural network (CNN) was trained to predict the composition of both pure and mixed stones.
MRI-Guided High Intensity Focused Ultrasound of Liver and Kidney
High Intensity Focused Ultrasound (HIFU) can be used to achieve a local temperature increase deep inside the human body in a non-invasive way.
Deep correction of breathing-related artifacts in real-time MR-thermometry
Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information.
Patch-based field-of-view matching in multi-modal images for electroporation-based ablations
This can be achieved by the means of multi-modal deformable image registration (DIR), demonstrated to be capable of estimating dense and elastic deformations between images acquired by multiple imaging devices.
RegQCNET: Deep Quality Control for Image-to-template Brain MRI Affine Registration
In the current study, a compact 3D convolutional neural network (CNN), referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template.
AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation
Finally, we showed the interest of using semi-supervised learning to improve the performance of our method.
AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation
Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images.
