Effect of Speckle Filtering in the Performance of Segmentation of Ultrasound Images Using CNNs

The convolutional neural networks (CNNs) as tools for ultrasound image segmentation often have their performance affected by the low signal-to-noise ratio of the images. This prevents a correct classification and extraction of relevant information and therefore affects clinical diagnosis. We propose a study of the effect of different speckle filtering methods on CNN performance. For the proposed metrics (Jaccard coefficient and BF-Score), it was obtained that the SRAD filter exhibited the best behavior even in the lowest quality data. In addition, the lowest values were obtained for the standard deviation and variance, which translates into lower data dispersion, better repeatability, and, therefore, greater confidence in its accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG

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

Automatic analysis in echocardiography using machine learning

Echocardiography is the cornerstone of modern cardiac imaging due to its availability, low cost and real-time functionality. The modality has enabled sophisticated non-invasive evaluation of the hearts morphophysiology, with a wide range of clinical parameters of high diagnostic and prognostic value. However, despite the clinical impact, quantitative measurements are often omitted in clinical practice by being labor intensive, time consuming and difficult to reproduce. Automation can reduce some of these limitations and redefine parts of the clinical workflow, but the design of generic algorithms is complex due to the inherent variability of echocardiography data and the expertise required for interpretation. The overall goal of this work was to investigate the use of deep learning (DL) methods for fully automating several image analysis steps of an echocardiography exam. Emphasis was given to method adaptation for ultrasound (US) image processing, as well as addressing fundamental domain limitations such as noise and acquisition variability. Real-time support and workflow enhancements was also important features in the development. The thesis consists of three technical contributions and one clinical feasibility study. In the first part, a method for cardiac view classification with convolutional neural networks (CNNs) is presented. Further, we describe a recurrent CNN method for cardiac event detection. The third part presents a DL based motion estimator, and the integration of several DL components into a pipeline for automated longitudinal strain (LS) measurements. The last part is dedicated to a feasibility study comparing the latter with a commercially available solution. Results indicate that the different components can benefit or even be improved with DL. The flexibility of learning-based approaches helps to surpass conventional methods on inherent limitations of US. Integrating DL components in a pipeline for fully automated measurements was feasible, and yielded encouraging results by being comparable to intervendor variability. Despite several limitations described in the thesis, we can be optimistic about the future employment of DL in echocardiography.“In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of NTNU’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements