19 research outputs found
Improving Catheter Segmentation & Localization in 3D Cardiac Ultrasound Using Direction-Fused FCN
Fast and accurate catheter detection in cardiac catheterization using
harmless 3D ultrasound (US) can improve the efficiency and outcome of the
intervention. However, the low image quality of US requires extra training for
sonographers to localize the catheter. In this paper, we propose a catheter
detection method based on a pre-trained VGG network, which exploits 3D
information through re-organized cross-sections to segment the catheter by a
shared fully convolutional network (FCN), which is called a Direction-Fused FCN
(DF-FCN). Based on the segmented image of DF-FCN, the catheter can be localized
by model fitting. Our experiments show that the proposed method can
successfully detect an ablation catheter in a challenging ex-vivo 3D US
dataset, which was collected on the porcine heart. Extensive analysis shows
that the proposed method achieves a Dice score of 57.7%, which offers at least
an 11.8 % improvement when compared to state-of-the-art instrument detection
methods. Due to the improved segmentation performance by the DF-FCN, the
catheter can be localized with an error of only 1.4 mm.Comment: ISBI 2019 accepte
3D shape instantiation for intra-operative navigation from a single 2D projection
Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeon’s view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs).
For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies.
For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed.
For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique.
The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces
Is attention all you need in medical image analysis? A review
Medical imaging is a key component in clinical diagnosis, treatment planning
and clinical trial design, accounting for almost 90% of all healthcare data.
CNNs achieved performance gains in medical image analysis (MIA) over the last
years. CNNs can efficiently model local pixel interactions and be trained on
small-scale MI data. The main disadvantage of typical CNN models is that they
ignore global pixel relationships within images, which limits their
generalisation ability to understand out-of-distribution data with different
'global' information. The recent progress of Artificial Intelligence gave rise
to Transformers, which can learn global relationships from data. However, full
Transformer models need to be trained on large-scale data and involve
tremendous computational complexity. Attention and Transformer compartments
(Transf/Attention) which can well maintain properties for modelling global
relationships, have been proposed as lighter alternatives of full Transformers.
Recently, there is an increasing trend to co-pollinate complementary
local-global properties from CNN and Transf/Attention architectures, which led
to a new era of hybrid models. The past years have witnessed substantial growth
in hybrid CNN-Transf/Attention models across diverse MIA problems. In this
systematic review, we survey existing hybrid CNN-Transf/Attention models,
review and unravel key architectural designs, analyse breakthroughs, and
evaluate current and future opportunities as well as challenges. We also
introduced a comprehensive analysis framework on generalisation opportunities
of scientific and clinical impact, based on which new data-driven domain
generalisation and adaptation methods can be stimulated
Quantifying atherosclerosis in vasculature using ultrasound imaging
Cerebrovascular disease accounts for approximately 30% of the global burden
associated with cardiovascular diseases [1]. According to the World Stroke
Organisation, there are approximately 13.7 million new stroke cases annually,
and just under six million people will die from stroke each year [2]. The
underlying cause of this disease is atherosclerosis – a vascular pathology
which is characterised by thickening and hardening of blood vessel walls.
When fatty substances such as cholesterol accumulate on the inner linings of
an artery, they cause a progressive narrowing of the lumen referred to as a
stenosis.
Localisation and grading of the severity of a stenosis, is important for
practitioners to assess the risk of rupture which leads to stroke. Ultrasound
imaging is popular for this purpose. It is low cost, non-invasive, and permits a
quick assessment of vessel geometry and stenosis by measuring the intima
media thickness. Research is showing that 3D monitoring of plaque
progression may provide a better indication of sites which are at risk of
rupture. Various metrics have been proposed. From these, the quantification
of plaques by measuring vessel wall volume (VWV) using the segmented
media-adventitia boundaries (MAB) and lumen-intima boundaries (LIB) has
been shown to be sensitive to temporal changes in carotid plaque burden.
Thus, methods to segment these boundaries are required to help generate
VWV measurements with high accuracy, less user interaction and increased
robustness to variability in di↵erent user acquisition protocols.ii
This work proposes three novel methods to address these requirements, to
ultimately produce a highly accurate, fully automated segmentation algorithm
which works on intensity-invariant data. The first method proposed was that
of generating a novel, intensity-invariant representation of ultrasound data by
creating phase-congruency maps from raw unprocessed radio-frequency
ultrasound information. Experiments carried out showed that this
representation retained the necessary anatomical structural information to
facilitate segmentation, while concurrently being invariant to changes in
amplitude from the user. The second method proposed was the novel
application of Deep Convolutional Networks (DCN) to carotid ultrasound
images to achieve fully automatic delineation of the MAB boundaries, in
addition to the use of a novel fusion of amplitude and phase congruency data
as an image source. Experiments carried out showed that the DCN produces
highly accurate and automated results, and that the fusion of amplitude and
phase yield superior results to either one alone. The third method proposed
was a new geometrically constrained objective function for the network's
Stochastic Gradient Descent optimisation, thus tuning it to the segmentation
problem at hand, while also developing the network further to concurrently
delineate both the MAB and LIB to produce vessel wall contours. Experiments
carried out here also show that the novel geometric constraints improve the
segmentation results on both MAB and LIB contours.
In conclusion, the presented work provides significant novel contributions to
field of Carotid Ultrasound segmentation, and with future work, this could lead
to implementations which facilitate plaque progression analysis for the end�user