869 citations · 1.5k across the 13 of their papers we have counts for
13 papers
Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain
Chiara Di Vece, Maela Le Lous, Brian Dromey +4
In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significa…
PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction
Aya Mutaz Zeidan, Paula Ramirez Gilliland, Ashay Patel +8
Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the appl…
Learning-Based Keypoint Registration for Fetoscopic Mosaicking
Alessandro Casella, Sophia Bano, Francisco Vasconcelos +7
In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the two fetuses. In the current pract…
BiometryNet: Landmark-based Fetal Biometry Estimation from Standard Ultrasound Planes
Netanell Avisdris, Leo Joskowicz, Brian Dromey +5
Fetal growth assessment from ultrasound is based on a few biometric measurements that are performed manually and assessed relative to the expected gestational age. Reliable biometr…
A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation
Lucas Fidon, Michael Aertsen, Florian Kofler +18
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, wi…
Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
Lucas Fidon, Michael Aertsen, Nada Mufti +13
The performance of deep neural networks typically increases with the number of training images. However, not all images have the same importance towards improved performance and ro…
AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes
Sophia Bano, Brian Dromey, Francisco Vasconcelos +4
During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonog…
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
Lucas Fidon, Michael Aertsen, Doaa Emam +9
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. Methods…
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Sophia Bano, Alessandro Casella, Francisco Vasconcelos +9
Fetoscopy laser photocoagulation is a widely used procedure for the treatment of Twin-to-Twin Transfusion Syndrome (TTTS), that occur in mono-chorionic multiple pregnancies due to…
Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
Sophia Bano, Francisco Vasconcelos, Luke M. Shepherd +6
During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser…
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Lucas Fidon, Michael Aertsen, Thomas Deprest +14
Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distribut…
Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning
Guotai Wang, Wenqi Li, Maria A. Zuluaga +8
Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate…
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang, Maria A. Zuluaga, Wenqi Li +8
Accurate medical image segmentation is essential for diagnosis, surgical planning and many other applications. Convolutional Neural Networks (CNNs) have become the state-of-the-art…