660 research outputs found
Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound
Standard plane localization is crucial for ultrasound (US) diagnosis. In
prenatal US, dozens of standard planes are manually acquired with a 2D probe.
It is time-consuming and operator-dependent. In comparison, 3D US containing
multiple standard planes in one shot has the inherent advantages of less
user-dependency and more efficiency. However, manual plane localization in US
volume is challenging due to the huge search space and large fetal posture
variation. In this study, we propose a novel reinforcement learning (RL)
framework to automatically localize fetal brain standard planes in 3D US. Our
contribution is two-fold. First, we equip the RL framework with a
landmark-aware alignment module to provide warm start and strong spatial bounds
for the agent actions, thus ensuring its effectiveness. Second, instead of
passively and empirically terminating the agent inference, we propose a
recurrent neural network based strategy for active termination of the agent's
interaction procedure. This improves both the accuracy and efficiency of the
localization system. Extensively validated on our in-house large dataset, our
approach achieves the accuracy of 3.4mm/9.6{\deg} and 2.7mm/9.1{\deg} for the
transcerebellar and transthalamic plane localization, respectively. Ourproposed
RL framework is general and has the potential to improve the efficiency and
standardization of US scanning.Comment: 9 pages, 5 figures, 1 table. Accepted by MICCAI 2019 (oral
Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound
3D ultrasound (US) is widely used due to its rich diagnostic information,
portability and low cost. Automated standard plane (SP) localization in US
volume not only improves efficiency and reduces user-dependence, but also
boosts 3D US interpretation. In this study, we propose a novel Multi-Agent
Reinforcement Learning (MARL) framework to localize multiple uterine SPs in 3D
US simultaneously. Our contribution is two-fold. First, we equip the MARL with
a one-shot neural architecture search (NAS) module to obtain the optimal agent
for each plane. Specifically, Gradient-based search using Differentiable
Architecture Sampler (GDAS) is employed to accelerate and stabilize the
training process. Second, we propose a novel collaborative strategy to
strengthen agents' communication. Our strategy uses recurrent neural network
(RNN) to learn the spatial relationship among SPs effectively. Extensively
validated on a large dataset, our approach achieves the accuracy of 7.05
degree/2.21mm, 8.62 degree/2.36mm and 5.93 degree/0.89mm for the mid-sagittal,
transverse and coronal plane localization, respectively. The proposed MARL
framework can significantly increase the plane localization accuracy and reduce
the computational cost and model size.Comment: Early accepted by MICCAI 202
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
Funded by the Spanish Ministry of Economic Affairs and Digital Transformation (Project MIA.2021.M02.0005 TARTAGLIA, from the Recovery, Resilience, and Transformation Plan financed by the European Union through Next Generation EU funds). TARTAGLIA takes place under the R&D Missions in Artificial Intelligence program, which is part of the Spain Digital 2025 Agenda and the Spanish National Artificial Intelligence Strategy.Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEMinistry of Economic Affairs and Digital Transformation from the Recovery, Resilience, and Transformation PlanNext Generation EU fundspu
AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI
Multi-slice magnetic resonance images of the fetal brain are usually
contaminated by severe and arbitrary fetal and maternal motion. Hence, stable
and robust motion correction is necessary to reconstruct high-resolution 3D
fetal brain volume for clinical diagnosis and quantitative analysis. However,
the conventional registration-based correction has a limited capture range and
is insufficient for detecting relatively large motions. Here, we present a
novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM)
correction of the multi-slice fetal brain MRI. It learns the sequential motion
from multiple stacks of slices and integrates the features between 2D slices
and reconstructed 3D volume using affinity fusion, which resembles the
iterations between slice-to-volume registration and volumetric reconstruction
in the regular pipeline. The method accurately estimates the motion regardless
of brain orientations and outperforms other state-of-the-art learning-based
methods on the simulated motion-corrupted data, with a 48.4% reduction of mean
absolute error for rotation and 61.3% for displacement. We then incorporated
AFFIRM into the multi-resolution slice-to-volume registration and tested it on
the real-world fetal MRI scans at different gestation stages. The results
indicated that adding AFFIRM to the conventional pipeline improved the success
rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%
Visual Feature Attribution using Wasserstein GANs
Attributing the pixels of an input image to a certain category is an
important and well-studied problem in computer vision, with applications
ranging from weakly supervised localisation to understanding hidden effects in
the data. In recent years, approaches based on interpreting a previously
trained neural network classifier have become the de facto state-of-the-art and
are commonly used on medical as well as natural image datasets. In this paper,
we discuss a limitation of these approaches which may lead to only a subset of
the category specific features being detected. To address this problem we
develop a novel feature attribution technique based on Wasserstein Generative
Adversarial Networks (WGAN), which does not suffer from this limitation. We
show that our proposed method performs substantially better than the
state-of-the-art for visual attribution on a synthetic dataset and on real 3D
neuroimaging data from patients with mild cognitive impairment (MCI) and
Alzheimer's disease (AD). For AD patients the method produces compellingly
realistic disease effect maps which are very close to the observed effects.Comment: Accepted to CVPR 201
- …