Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

Bhalodia, Riddhish; Goparaju, Anupama; Sodergren, Tim; Morris, Alan; Kholmovski, Evgueni; Marrouche, Nassir F.; Cates, Joshua; Whitaker, Ross T.; Elhabian, Shireen

doi:10.22489/cinc.2018.160

Cited by 12 publications

(13 citation statements)

References 19 publications

(17 reference statements)

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“…These sets of particles are then projected to a low-dimensional shape representation using PCA to facilitate subsequent analysis [27]. Some recent works leverage convolution neural networks (CNNs) to perform regression from images to a shape description of these dense correspondences [5,7]. These methods are supervised and require an existing shape model or manual landmarks for their training.…”

Section: Related Workmentioning

confidence: 99%

Self-supervised Discovery of Anatomical Shape Landmarks

Bhalodia

Kavan

Whitaker

2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Statistical shape analysis is a very useful tool in a wide range of medical and biological applications. However, it typically relies on the ability to produce a relatively small number of features that can capture the relevant variability in a population. State-of-the-art methods for obtaining such anatomical features rely on either extensive preprocessing or segmentation and/or significant tuning and post-processing. These shortcomings limit the widespread use of shape statistics. We propose that effective shape representations should provide sufficient information to align/register images. Using this assumption we propose a self-supervised, neural network approach for automatically positioning and detecting landmarks in images that can be used for subsequent analysis. The network discovers the landmarks corresponding to anatomical shape features that promote good image registration in the context of a particular class of transformations. In addition, we also propose a regularization for the proposed network which allows for a uniform distribution of these discovered landmarks. In this paper, we present a complete framework, which only takes a set of input images and produces landmarks that are immediately usable for statistical shape analysis. We evaluate the performance on a phantom dataset as well as 2D and 3D images.

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Section: Related Workmentioning

confidence: 99%

Self-supervised Discovery of Anatomical Shape Landmarks

Bhalodia

Kavan

Whitaker

2020

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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“…This model can then be used to generate deformations within the range of plausible parameters. This approach has demonstrated an improvement in segmentation performance 123–127 …”

Section: Methodsmentioning

confidence: 99%

“…This approach has demonstrated an improvement in segmentation performance. [123][124][125][126][127] Other deformable augmentation techniques Javaid et al 37 proposed a methodology for CT segmentation that aimed to simulate intra-and interobserver variability. In addition to basic and elastic deformations, they augmented the contours made on the training data, rather than the images themselves.…”

Section: Statistical Shape Modelsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…Another method of quantifying shape differences is using the coordinate transformations that align the population of images/shapes to a predefined atlas [4]. These automated methods for representing shapes and their subsequent analysis finds application in several medical domains, such as orthopedics [5,26], implant design [23,34], neuroscience [19,56], and cardiology [8,18].…”

Section: Introductionmentioning

confidence: 99%

DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models

Bhalodia¹,

Elhabian²,

Adams³

et al. 2021

Preprint

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Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and nonlinear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

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Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation

Cited by 12 publications

References 19 publications

Self-supervised Discovery of Anatomical Shape Landmarks

Self-supervised Discovery of Anatomical Shape Landmarks

A review of medical image data augmentation techniques for deep learning applications

DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models

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