424 research outputs found
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomalies
The detection and localization of anomalies is one important medical image
analysis task. Most commonly, Computer Vision anomaly detection approaches rely
on manual annotations that are both time consuming and expensive to obtain.
Unsupervised anomaly detection, or Out-of-Distribution detection, aims at
identifying anomalous samples relying only on unannotated samples considered
normal. In this study we present a new unsupervised anomaly detection method.
Our method builds upon the self-supervised strategy consisting on training a
segmentation network to identify local synthetic anomalies. Our contributions
improve the synthetic anomaly generation process, making synthetic anomalies
more heterogeneous and challenging by 1) using complex random shapes and 2)
smoothing the edges of synthetic anomalies so networks cannot rely on the high
gradient between image and synthetic anomalies. In our implementation we
adopted standard practices in 3D medical image segmentation, including 3D U-Net
architecture, patch-wise training and model ensembling. Our method was
evaluated using a validation set with different types of synthetic anomalies.
Our experiments show that our method improved substantially the baseline method
performance. Additionally, we evaluated our method by participating in the
Medical Out-of-Distribution (MOOD) Challenge held at MICCAI in 2022 and
achieved first position in both sample-wise and pixel-wise tasks. Our
experiments and results in the latest MOOD challenge show that our simple yet
effective approach can substantially improve the performance of
Out-of-Distribution detection techniques which rely on synthetic anomalies.Comment: 15 pages, 6 figure
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Bis(2-hydroxyethyl) 2-phenylsuccinate
Succinic acid esters are important compounds that find many applications in various industrial fields. One of the most promising and easy ways of producing these molecules is represented by the bis-alkoxycarbonylation reaction of olefins. In particular, a recently developed catalytic
system, consisting of an aryl alfa-diimine/palladium(II) catalyst and p-benzoquinone as an oxidant, has allowed succinates to be obtained in high yields. A similar methodology was applied here for the unprecedented synthesis of the bis(2-hydroxyethyl) 2-phenylsuccinate in 78% isolated yield, starting from the cheap and commercially available compounds styrene and ethylene glycol. To our knowledge, no other examples of bis-alkoxycarbonylations of olefins involving diols have been
reported thus far. The obtained product was fully characterized by NMR and ESI-MS analyses
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Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes. Up to 14.2% of the analysed SA stacks had sub-optimal coverage (i.e. missing basal and/or apical slices), however most of them were limited to the first year of acquisition. Up to 16% of the stacks were affected by noticeable inter-slice motion (i.e. average inter-slice misalignment greater than 3.4 mm). Inter-slice motion was positively correlated with weight and body surface area. Only 2.1% of the stacks had an average end-diastolic cardiac image contrast below 30% of the dynamic range. These findings will be highly valuable for both the scientists involved in UKBB CMR acquisition and for the ones who use the dataset for research purposes
MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying
anomalous regions in images leveraging only models trained on images of healthy
anatomy. An established approach is to tokenize images and model the
distribution of tokens with Auto-Regressive (AR) models. AR models are used to
1) identify anomalous tokens and 2) in-paint anomalous representations with
in-distribution tokens. However, AR models are slow at inference time and prone
to error accumulation issues which negatively affect OOD detection performance.
Our novel method, MIM-OOD, overcomes both speed and error accumulation issues
by replacing the AR model with two task-specific networks: 1) a transformer
optimized to identify anomalous tokens and 2) a transformer optimized to
in-paint anomalous tokens using masked image modelling (MIM). Our experiments
with brain MRI anomalies show that MIM-OOD substantially outperforms AR models
(DICE 0.458 vs 0.301) while achieving a nearly 25x speedup (9.5s vs 244s).Comment: 12 pages, 5 figures. Accepted in DGM4MICCAI workshop @ MICCAI 202
A Stimulated Emission Study of the Ground State Bending Levels of BH\u3csub\u3e2\u3c/sub\u3e Through the Barrier to Linearity and \u3cem\u3eAb Initio\u3c/em\u3e Calculations of Near-Spectroscopic Accuracy
The ground state bending levels of 11BH2 have been studied experimentally using a combination of low-resolution emission spectroscopy and high-resolution stimulated emission pumping (SEP) measurements. The data encompass the energy range below, through, and above the calculated position of the barrier to linearity. For the bending levels (0,3,0) and above, the data show substantial K-reordering, with the K a = 1 levels falling well below those with K a = 0. A comparison of the high-resolution rotationally resolved SEP data to our own very high level ab initio calculations of the rovibronic energy levels shows agreement approaching near-spectroscopic accuracy (a few cm−1). The data reported in this work provide very stringent tests for future theoretical treatments of this prototypical seven-electron free radical
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Learning under Distributed Weak Supervision
The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
Quality assessment of medical images is essential for complete automation of
image processing pipelines. For large population studies such as the UK
Biobank, artefacts such as those caused by heart motion are problematic and
manual identification is tedious and time-consuming. Therefore, there is an
urgent need for automatic image quality assessment techniques. In this paper,
we propose a method to automatically detect the presence of motion-related
artefacts in cardiac magnetic resonance (CMR) images. As this is a highly
imbalanced classification problem (due to the high number of good quality
images compared to the low number of images with motion artefacts), we propose
a novel k-space based training data augmentation approach in order to address
this problem. Our method is based on 3D spatio-temporal Convolutional Neural
Networks, and is able to detect 2D+time short axis images with motion artefacts
in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset
consisting of 3465 CMR images and achieve not only high accuracy in detection
of motion artefacts, but also high precision and recall. We compare our
approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc
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