Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks

To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.Comment: 13 pages, 7 figures, MIUA conferenc

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

Fully-automated deep learning pipeline for 3D fetal brain ultrasound

Three-dimensional ultrasound (3D US) imaging has shown significant potential for in-utero assessment of the development of the fetal brain. However, in spite of the potential benefits of this modality over its two-dimensional (2D) counterpart, its widespread adoption remains largely limited by the difficulty associated with its analysis. While more established 3D neuroimaging modalities, such as Magnetic Res- onance Imaging (MRI), have circumvented similar challenges thanks to reliable, automated neuroimage analysis pipelines, there is currently no comparable pipeline solution for 3D neurosonography. With the goal of facilitating medical research and encouraging the adoption of 3D US for clinical assessment, the main objective of my doctoral thesis is to design, develop, and validate a set of fundamental automated modules that comprise a fast, robust, fully automated, general-purpose pipeline for the neuroimage analysis of fetal 3D US scans. For the first module, I propose the fetal Brain Extraction Network (fBEN), a fully-automated, end-to-end 3D Convolutional Neural Network (CNN) with an encoder-decoder architecture. It predicts an accurate binary brain mask for the automated extraction of the fetal brain from standard clinical 3D US scans. For the second module I propose the fetal Brain Alignment Network (fBAN), a fully-automated, end-to-end regression network with a cascade architecture that accurately predicts the alignment parameters required to rigidly align standard clinical 3D US scans to a canonical reference space. Finally, for the third module, I propose the fetal Brain Fingerprinting Net- work (fBFN), a fully-automated, end-to-end network based on a Variational AutoEncoder (VAE) architecture, that encodes the entire structural information of the 3D brain into a relatively small set of parameters in a continuously distributed latent space. It is a general-purpose solution aimed at facilitating the assessment of the 3D US scans by recharacterising the fetal brain into a representation that is easier to analyse. After exhaustive analysis, each module of this pipeline has proven to achieve state-of-the-art performance that is consistent across a wide gestational range, as well as robust to image quality, while requiring minimal pre-processing. Additionally, this pipeline has been designed to be modular, and easy to modify and expand upon, with the purpose of making it as easy as possible for other researchers to develop new tools and adapt it to their needs. This combination of performance, flexibility, and ease of use may have the potential to help 3D US become the preferred imaging modality for researching and assessing fetal development

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Deep learning techniques to bridge the gap between 2D and 3D ultrasound imaging

Three-dimensional (3D) ultrasound imaging has contributed to our understanding of fetal developmental processes in the womb by providing rich contextual information of the inherently 3D anatomies. However, its use is limited in clinical settings, due to the high purchasing costs and limited diagnostic practicality. Freehand two-dimensional (2D) ultrasound imaging, in contrast, is routinely used in standard obstetric exams. The low cost and portability of 2D ultrasound render it uniquely suitable for use in low- and middle-income settings. However, high level of expertise is always involved and it inherently lacks a 3D representation of the anatomies, which limit its potential for more accessible and advanced assessment. Capitalizing on the flexibility offered by freehand 2D ultrasound acquisition, this thesis presents a deep learning-based framework for optimizing the utilization and diagnostic power of 2D freehand ultrasound in fetal brain imaging. First, a localization model is presented to predict the location of 2D ultrasound fetal brain scans in the 3D brain atlas. It is trained by sampling 2D slices from aligned 3D fetal brain volumes, such that heavy annotations for each 2D scan are not required. This can be used for scanning guidance and standard plane localization. An unsupervised methodology is further proposed to adapt a trained localization model to freehand 2D ultrasound images acquired from arbitrary domains, for example sonographers, manufacturers and acquisition protocols. This enables the model to be used at the bedside in practice, where it can be fine-tuned with just the images acquired in any arbitrary domains before inference. Building upon the ability to localize 2D scans in the 3D brain atlas, a framework is further presented to reconstruct 3D volumes from non-sensor-tracked 2D ultrasound images using implicit representation. With this slice-to-volume reconstruction framework, additional 3D information can be extracted from the 2D freehand scans. Finally, a semi-automatic model, trained only on raw 3D volumes without any manual annotation, is presented to segment any arbitrary structures of interest in 3D medical volumes, while only requiring manual annotation of a single slice during inference. The model is tested on wide variety of medical imaging datasets and anatomical structures, verifying its generalizability. In the design of the framework presented in this thesis, three fundamental principles, namely minimal human annotation, generalizability and sensorless operation, are followed to optimize its seamless integration into the clinical workflow. This may modernize freehand routine scanning and enhance its accessibility, while maximizing the clinical information gained from routine scans acquired as part of the continuum of pregnancy care

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

A review of image processing methods for fetal head and brain analysis in ultrasound images

Background and objective: Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. Methods: In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. Results: For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. Conclusions: A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection. (c) 2022 Elsevier B.V. All rights reserved.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work was funded by projects “NORTE-01–0145-FEDER- 0 0 0 059 , NORTE-01-0145-FEDER-024300 and “NORTE-01–0145- FEDER-0 0 0 045 , supported by Northern Portugal Regional Opera- tional Programme (Norte2020), under the Portugal 2020 Partner- ship Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020 and UIDP/05549/2020 . The authors also acknowledge support from FCT and the Euro- pean Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018 and SFRH/BD/136721/2018

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