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Found 6 papers, 3 papers with code
Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs
Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes due to oblique or lacking fissures.
Fully-automated deep learning slice-based muscle estimation from CT images for sarcopenia assessment
The output consisted of a segmented muscle mass on a CT slice at the level of L3 vertebra.
Automatic L3 slice detection in 3D CT images using fully-convolutional networks
The analysis of single CT slices extracted at the third lumbar vertebra (L3) has garnered significant clinical interest in the past few years, in particular in regards to quantifying sarcopenia (muscle loss).
Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes
Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Domain Adaptation for MRI Organ Segmentation using Reverse Classification Accuracy
The variations in multi-center data in medical imaging studies have brought the necessity of domain adaptation.
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth.
