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Convolutional Neural Networks for Medical Image Processing Applications Editor Şaban Öztürk Assoc. Prof. Electrical and Electronics Engineering Department Technology Faculty Amasya University Amasya, Turkey p, p, A SCIENCE PUBLISHERS BOOK A SCIENCE PUBLISHERS BOOK First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2023 Şaban Öztürk CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data (applied for) ISBN: 978-1-032-10400-3 (hbk) ISBN: 978-1-032-10401-0 (pbk) ISBN: 978-1-003-21514-1 (ebk) DOI: 10.1201/9781003215141 Typeset in Times New Roman by Radiant Productions Preface In addition to facilitating human life, technological developments increase the expectation of the quality of life. For this reason, artificial intelligence supported technological solutions, which are very common in the industry, are rapidly spreading to the medical field today. In this way, it becomes possible to find solutions to many diseases. Today, medical imaging devices have become the most important and widely used method in the diagnosis of diseases. This widespread use brings with it many difficulties in addition to increasing the workload of doctors. In addition to overcoming these difficulties, which are analyzed in detail in this book, early diagnosis, which plays a crucial role in human life, is possible with artificial intelligence methods. For this purpose, this book named ‘Convolutional Neural Networks for Medical Image Processing Applications’ covers the use of today’s most effective artificial intelligence methods for real medical image analysis. Contents Preface iii 1. Convolutional Neural Networks for Segmentation in Short-Axis 1 Cine Cardiac Magnetic Resonance Imaging: Review and Considerations Manuel Pérez‑Pelegrí, José V. Monmeneu, María P. López‑Lereu and David Moratal 2. Deep Learning-Based Computer-Aided Diagnosis System 34 for Attention Deficit Hyperactivity Disorder Classification Using Synthetic Data Gulay Cicek and Aydın AKAN 3. Basic Ensembles of Vanilla-Style Deep Learning Models 52 Improve Liver Segmentation from CT Images A. Emre Kavur, Ludmila I. Kuncheva and M. Alper Selver 4. Convolutional Neural Networks for Medical Image Analysis 75 Rajesh Gogineni and Ashvini Chaturvedi 5. Ulcer and Red Lesion Detection in Wireless Capsule 91 Endoscopy Images using CNN Said Charfi, Mohamed El Ansari, Ayoub Ellahyani and Ilyas El Jaafari 6. Do More With Less: Deep Learning in Medical Imaging 109 Shivani Rohilla, Mahipal Jadeja and Emmanuel S. Pilli 7. Automatic Classification of fMRI Signals from Behavioral, 133 Cognitive and Affective Tasks Using Deep Learning Cemre Candemir, Osman Tayfun Bişkin, Mustafa Alper Selver and Ali Saffet Gönül vi ■ Convolutional Neural Networks for Medical Image Processing Applications 8. Detection of COVID-19 in Lung CT-Scans using 155 Reconstructed Image Features Ankita Sharma and Preety Singh 9. Dental Image Analysis: Where Deep Learning Meets Dentistry 170 Bernardo Silva, Laís Pinheiro, Katia Andrade, Patrícia Cury and Luciano Oliveira 10. Malarial Parasite Detection in Blood Smear Microscopic 196 Images: A Review on Deep Learning Approaches Kinde Anlay Fante and Fetulhak Abdurahman 11. Automatic Classification of Coronary Stenosis using 227 Convolutional Neural Networks and Simulated Annealing Luis Diego Rendon‑Aguilar, Ivan Cruz‑Aceves, Arturo Alfonso Fernandez‑Jaramillo, Ernesto Moya‑Albor, Jorge Brieva and Hiram Ponce 12. Deep Learning Approach for Detecting COVID-19 from 248 Chest X-ray Images Murali Krishna Puttagunta, S. Ravi and C. Nelson Kennedy Babu Index 267 Chapter 1 Convolutional Neural Networks for Segmentation in Short-Axis Cine Cardiac Magnetic Resonance Imaging Review and Considerations Manuel Pérez-Pelegrí,1 José V. Monmeneu,2 María P. López-Lereu2 and David Moratal1,* ABSTRACT The characterization of the heart function and anatomy requires the segmentation of the main regions at both the systole and diastole. This is usually done by means of magnetic resonance image (MRI) cine sequences, usually with short-axis views of the heart. Convolutional neural networks (cnn) can be employed to achieve the segmentation of the desired regions of interest. This chapter describes how to design and apply convolutional neural networks for the task of segmentation in short-axis cardiac MRI. It covers the main features that characterize the image modality and an overview of the segmentation problem with (cnn). 1 Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Valencia, Spain. 2 Unidad de Imagen Cardíaca, ERESA-ASCIRES Grupo Biomédico, Valencia, Spain. * Corresponding author: [email protected] 2 ■ Convolutional Neural Networks for Medical Image Processing Applications The most popular segmentation models are introduced and the most relevant advances done in them are described. A review of the problem at hand is conducted along an exposition of the current state of the art solutions with cnn in cardiac MRI. Several key elements in the development of cnn for segmentation are discussed, including, selection of loss functions, how to tackle the segmentation problem when small datasets are available, the overfitting problem, approaches for better segmentation depending on the target within the images, and best practices for implementation of segmentation architectures in cine cardiac MRI. 1 Introduction Heart and cardiovascular diseases are some of the most extended causes of death and morbidity in advanced countries (Townsend et al., 2016). In this context cardiac magnetic resonance imaging (MRI) is an advanced and useful imaging modality (De Roos and Higgins, 2014; Finn et al., 2006) that can give clinicians relevant information of both the structure and the functionality of the heart tissues and is considered the best imaging modality for assessing the heart (La Gerche et al., 2013; Seraphim et al., 2020). More specifically cardiac short-axis cine MRI acquisitions are especially useful, as they provide a way to visualize different structures of the heart with great contrast and are easy to interpret and to extract segmentation volumes of the regions of interest. However, segmentation is a hard and time consuming task, even with semi-automatic softwares which are often employed in the clinical setting. In this setting convolutional neural networks have shown a great potential to solve the problem of cardiac segmentation in short-axis cine MRI with very accurate results. Convolutional neural networks (cnn) are a type of deep learning architecture especially designed to treat images. They can be employed to different problem types, and this includes segmentation problems. Convolutional neural networks started to be of interest after the AlexNet convolutional network significantly outperformed all the rival algorithms at the annual ImageNet competition in 2012 (Krizhevsky et al., 2017). Ever since then, convolutional neural networks have increasingly received more attention in the medical imaging field, and in more recent years this also includes cardiac imaging. Segmentation convolutional neural networks have been continuously developed since the first full convolutional neural network (FCN) was described (Long et al., 2015), and nowadays it is one of the fields where deep learning has been more focused in the medical imaging community. CNN for Segmentation in Cardiac MRI ■ 3 The impact of these algorithms in medical image processing has been growing in later years, and this has also included cardiac MRI. After a search in PubMed (https://pubmed.ncbi.nlm.nih.gov/) with different key words, it can be seen how the growing interest in convolutional neural networks in the medical field has been increasing over the years. Figure 1 shows graphics with the number of publications obtained by year with the key words “convolutional neural networks”, “convolutional neural networks segmentation” and “convolutional neural networks cardiac MRI”. Figure 1: Number of publications by year registered in PubMed with keyword “Convolutional neural networks” (left), “Convolutional neural networks segmentation” (middle) and “Convolutional neural networks cardiac MRI” (right). In all cases we can see that interest has been growing at an exponential rate for generic, segmentation and specifically for cardiac MRI problems. The exponential growth seems to start clearly around 2017 with no apparent decrease in interest nowadays. In the following sections of this chapter the problem of segmentation in cardiac short-axis cine MRI with convolutional neural networks is discussed in detail along with important recent advances and applications of interest that could be employed for the design of architectures to segment the regions of interest within the images. 2 Short-Axis Cine MR Imaging Cine MRI is a type of imaging that is capable of capturing motion. In the case of cardiac MRI, it provides a tool for obtaining images of the heart’s motion and enables the visualization of the cardiac cycle. The acquisition of these images is done synchronously with ECG-gating to alleviate the intrinsic movement of the heart. The reconstruction is usually done retrospectively, after a continuous set of simultaneous acquisitions from both the MRI and ECG signals. As the lung also produces motion during the respiratory cycle, the acquisitions are usually done in breath-hold moments. Multiple breath holds are normally required for fully reconstructing the heart in the images.