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Found 10 papers, 1 papers with code
Test-time augmentation-based active learning and self-training for label-efficient segmentation
However, while self-training improved the performance of single-sequence fetal body segmentation when combined with AL, it slightly deteriorated performance of multi-sequence placenta segmentation on ID data.
Simultaneous column-based deep learning progression analysis of atrophy associated with AMD in longitudinal OCT studies
Methods: We present a fully automatic end-to-end pipeline for the simultaneous detection and quantification of time-related atrophy changes associated with dry AMD in pairs of OCT scans of a patient.
Contour Dice loss for structures with Fuzzy and Complex Boundaries in Fetal MRI
In this paper, we study the use of the Contour Dice loss for both problems and compare it to other boundary losses and to the combined Dice and Cross-Entropy loss.
Partial annotations for the segmentation of large structures with low annotation cost
We tested the method with the popular soft Dice loss for the fetal body segmentation task in two MRI sequences, TRUFI and FIESTA, and compared full annotation regime to partial annotations with a similar annotation effort.
Automatic fetal fat quantification from MRI
It consists of two steps: 1) model-based semi-automatic fetal fat segmentations, reviewed and corrected by a radiologist; 2) automatic fetal fat segmentation using DL networks trained on the resulting annotated dataset.
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
Comparison and cross-validation of three different biometric measurements on two independent datasets shows that BiometryNet is robust and yields accurate measurements whose errors are lower than the clinically permissible errors, outperforming other existing automated biometry estimation methods.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
Automatic linear measurements of the fetal brain on MRI with deep neural networks
Fetal MRI is increasingly used for fetal brain assessment.
Automatic detection and diagnosis of sacroiliitis in CT scans as incidental findings
The algorithm consists of four steps: 1) computation of an initial region of interest (ROI) that includes the pelvic joints region using heuristics and a U-Net classifier; 2) refinement of the ROI to detect both sacroiliac joints using a four-tree random forest; 3) individual sacroiliitis grading of each sacroiliac joint in each CT slice with a custom slice CNN classifier, and; 4) sacroiliitis diagnosis and grading by combining the individual slice grades using a random forest.
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
