Search Results for author:
Found 12 papers, 7 papers with code
Measuring proximity to standard planes during fetal brain ultrasound scanning
This paper introduces a novel pipeline designed to bring ultrasound (US) plane pose estimation closer to clinical use for more effective navigation to the standard planes (SPs) in the fetal brain.
Learning-Based Keypoint Registration for Fetoscopic Mosaicking
This surgery is minimally invasive and relies on fetoscopy.
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.
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels.
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
The performance of deep neural networks typically increases with the number of training images.
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes
To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Deep neural networks have increased the accuracy of automatic segmentation, however, their accuracy depends on the availability of a large number of fully segmented images.
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos.
Distributionally Robust Deep Learning using Hardness Weighted Sampling
In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM).
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Experimental results show that 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine-tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
We propose a deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy.
