11 research outputs found
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes
Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images
In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes
Fully-automated root image analysis (faRIA)
High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool. © 2021, The Author(s)
Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms
Meningiomas are the most common type of primary brain tumor, accounting for
approximately 30% of all brain tumors. A substantial number of these tumors are
never surgically removed but rather monitored over time. Automatic and precise
meningioma segmentation is therefore beneficial to enable reliable growth
estimation and patient-specific treatment planning. In this study, we propose
the inclusion of attention mechanisms over a U-Net architecture: (i)
Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a
3D MRI volume as input. Attention has the potential to leverage the global
context and identify features' relationships across the entire volume. To limit
spatial resolution degradation and loss of detail inherent to encoder-decoder
architectures, we studied the impact of multi-scale input and deep supervision
components. The proposed architectures are trainable end-to-end and each
concept can be seamlessly disabled for ablation studies. The validation studies
were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes
from St. Olavs University Hospital, Trondheim, Norway. For the best performing
architecture, an average Dice score of 81.6% was reached for an F1-score of
95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3ml
were occasionally missed hence reaching an overall recall of 93%. Leveraging
global context from a 3D MRI volume provided the best performances, even if the
native volume resolution could not be processed directly. Overall, near-perfect
detection was achieved for meningiomas larger than 3ml which is relevant for
clinical use. In the future, the use of multi-scale designs and refinement
networks should be further investigated to improve the performance. A larger
number of cases with meningiomas below 3ml might also be needed to improve the
performance for the smallest tumors.Comment: 16 pages, 5 figures, 3 tables. Submitted to Artificial Intelligence
in Medicin
Neural architecture search of echocardiography view classifiers
Purpose: Echocardiography is the most commonly used modality for assessing the heart in
clinical practice. In an echocardiographic exam, an ultrasound probe samples the heart from
different orientations and positions, thereby creating different viewpoints for assessing the
cardiac function. The determination of the probe viewpoint forms an essential step in automatic
echocardiographic image analysis.
Approach: In this study, convolutional neural networks are used for the automated identification
of 14 different anatomical echocardiographic views (larger than any previous study) in a dataset
of 8732 videos acquired from 374 patients. Differentiable architecture search approach was
utilized to design small neural network architectures for rapid inference while maintaining high
accuracy. The impact of the image quality and resolution, size of the training dataset, and number
of echocardiographic view classes on the efficacy of the models were also investigated.
Results: In contrast to the deeper classification architectures, the proposed models had significantly lower number of trainable parameters (up to 99.9% reduction), achieved comparable
classification performance (accuracy 88.4% to 96%, precision 87.8% to 95.2%, recall 87.1%
to 95.1%) and real-time performance with inference time per image of 3.6 to 12.6 ms.
Conclusion: Compared with the standard classification neural network architectures, the proposed models are faster and achieve comparable classification performance. They also require
less training data. Such models can be used for real-time detection of the standard views
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
Medical Image Analysis is currently experiencing a paradigm shift due to Deep
Learning. This technology has recently attracted so much interest of the
Medical Imaging community that it led to a specialized conference in `Medical
Imaging with Deep Learning' in the year 2018. This article surveys the recent
developments in this direction, and provides a critical review of the related
major aspects. We organize the reviewed literature according to the underlying
Pattern Recognition tasks, and further sub-categorize it following a taxonomy
based on human anatomy. This article does not assume prior knowledge of Deep
Learning and makes a significant contribution in explaining the core Deep
Learning concepts to the non-experts in the Medical community. Unique to this
study is the Computer Vision/Machine Learning perspective taken on the advances
of Deep Learning in Medical Imaging. This enables us to single out `lack of
appropriately annotated large-scale datasets' as the core challenge (among
other challenges) in this research direction. We draw on the insights from the
sister research fields of Computer Vision, Pattern Recognition and Machine
Learning etc.; where the techniques of dealing with such challenges have
already matured, to provide promising directions for the Medical Imaging
community to fully harness Deep Learning in the future