Search Results for author:
Found 6 papers, 2 papers with code
MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences
Results: The model showcased high accuracy in segmenting well-defined organs, achieving Dice Similarity Coefficient (DSC) scores of 0. 97 for the right and left lungs, and 0. 95 for the heart.
Transfer learning from a sparsely annotated dataset of 3D medical images
A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available.
Kidney abnormality segmentation in thorax-abdomen CT scans
Our end-to-end segmentation method was trained on 215 contrast-enhanced thoracic-abdominal CT scans, with half of these scans containing one or more abnormalities.
Automated Estimation of Total Lung Volume using Chest Radiographs and Deep Learning
The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT.
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge
We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set.
Towards automatic pulmonary nodule management in lung cancer screening with deep learning
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy.
