Table Of ContentConvolutional 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
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© 2023 Şaban Öztürk
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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
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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.
