A benchmark for 2D foetal brain ultrasound analysis
- URL: http://arxiv.org/abs/2406.17250v1
- Date: Tue, 25 Jun 2024 03:34:54 GMT
- Title: A benchmark for 2D foetal brain ultrasound analysis
- Authors: Mariano Cabezas, Yago Diez, Clara Martinez-Diago, Anna Maroto,
- Abstract summary: We present a set of 104 2D foetal brain ultrasound images acquired during the 20th week of gestation.
The images have been annotated to highlight landmark points from structures of interest to analyse brain development.
- Score: 0.3742372933871118
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Brain development involves a sequence of structural changes from early stages of the embryo until several months after birth. Currently, ultrasound is the established technique for screening due to its ability to acquire dynamic images in real-time without radiation and to its cost-efficiency. However, identifying abnormalities remains challenging due to the difficulty in interpreting foetal brain images. In this work we present a set of 104 2D foetal brain ultrasound images acquired during the 20th week of gestation that have been co-registered to a common space from a rough skull segmentation. The images are provided both on the original space and template space centred on the ellipses of all the subjects. Furthermore, the images have been annotated to highlight landmark points from structures of interest to analyse brain development. Both the final atlas template with probabilistic maps and the original images can be used to develop new segmentation techniques, test registration approaches for foetal brain ultrasound, extend our work to longitudinal datasets and to detect anomalies in new images.
Related papers
- Cas-DiffCom: Cascaded diffusion model for infant longitudinal
super-resolution 3D medical image completion [47.83003164569194]
We propose a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution.
Experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion.
arXiv Detail & Related papers (2024-02-21T12:54:40Z) - FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans
Using Deep Self-supervised Learning [1.0819408603463427]
More than 140M fetuses are born yearly, resulting in numerous scans.
The availability of a large volume of fetal ultrasound scans presents the opportunity to train robust machine learning models.
This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views.
arXiv Detail & Related papers (2023-10-19T09:11:23Z) - Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy.
We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
arXiv Detail & Related papers (2023-03-07T09:40:22Z) - FetReg2021: A Challenge on Placental Vessel Segmentation and
Registration in Fetoscopy [52.3219875147181]
Fetoscopic laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS)
The procedure is particularly challenging due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility, and variability in illumination.
Computer-assisted intervention (CAI) can provide surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking.
Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fet
arXiv Detail & Related papers (2022-06-24T23:44:42Z) - SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID)
We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
arXiv Detail & Related papers (2021-11-26T13:47:34Z) - Fetal MRI by robust deep generative prior reconstruction and
diffeomorphic registration: application to gestational age prediction [5.491836552931295]
Volumetric reconstructions are proposed to correct for non-homogeneous and non-isotropic sampling factors.
Experiments are performed to validate our contributions and compare with a state of the art method.
Results suggest improved image resolution and more accurate prediction of gestational age at scan.
arXiv Detail & Related papers (2021-10-29T22:09:52Z) - Voice-assisted Image Labelling for Endoscopic Ultrasound Classification
using Neural Networks [48.732863591145964]
We propose a multi-modal convolutional neural network architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure.
Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels.
arXiv Detail & Related papers (2021-10-12T21:22:24Z) - FaBiAN: A Fetal Brain magnetic resonance Acquisition Numerical phantom [0.0]
We present FaBiAN, an open-source fetal magnetic resonance acquisition phantom.
We show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
arXiv Detail & Related papers (2021-09-06T22:37:55Z) - Developmental Stage Classification of EmbryosUsing Two-Stream Neural
Network with Linear-Chain Conditional Random Field [74.53314729742966]
We propose a two-stream model for developmental stage classification.
Unlike previous methods, our two-stream model accepts both temporal and image information.
We demonstrate our algorithm on two time-lapse embryo video datasets.
arXiv Detail & Related papers (2021-07-13T19:56:01Z) - Towards segmentation and spatial alignment of the human embryonic brain
using deep learning for atlas-based registration [3.8874909016794463]
We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework.
We trained this network end-to-end and validated it against a ground truth on synthetic datasets designed to resemble the challenges present in 3D first trimester ultrasound.
arXiv Detail & Related papers (2020-05-13T15:23:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.