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      Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

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          Abstract

          Deep learning-based image segmentation is by now firmly established as a robust tool in image segmentation. It has been widely used to separate homogeneous areas as the first and critical component of diagnosis and treatment pipeline. In this article, we present a critical appraisal of popular methods that have employed deep-learning techniques for medical image segmentation. Moreover, we summarize the most common challenges incurred and suggest possible solutions.

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          Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

          Hoo-Chang Shin, Holger R. Roth, Mingchen Gao … (2016)
          Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.
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            Deep Learning in Medical Image Analysis

            Dinggang Shen, Guorong Wu, Heung-Il Suk (2017)
            This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.
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              Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

              Nima Tajbakhsh, Jae Y. Shin, Suryakanth R Gurudu … (2016)
              Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.
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                Author and article information

                Contributors
                Mohammad Hesam Hesamian: +61 2 95147873 , mh.hesamian@gmail.com
                Journal
                Journal ID (nlm-ta): J Digit Imaging
                Journal ID (iso-abbrev): J Digit Imaging
                Title: Journal of Digital Imaging
                Publisher: Springer International Publishing (Cham )
                ISSN (Print): 0897-1889
                ISSN (Electronic): 1618-727X
                Publication date (Electronic): 29 May 2019
                Publication date PMC-release: 29 May 2019
                Publication date (Print): August 2019
                Volume: 32
                Issue: 4
                Pages: 582-596
                Affiliations
                [1 ]ISNI 0000 0004 1936 7611, GRID grid.117476.2, School of Electrical and Data Engineering (SEDE), , University of Technology Sydney, ; 2007 Sydney, Australia
                [2 ]ISNI 0000 0004 1936 7611, GRID grid.117476.2, CB11.09, , University of Technology Sydney, ; 81 Broadway, Ultimo NSW, 2007 Sydney, Australia
                [3 ]ISNI 0000 0004 1936 7611, GRID grid.117476.2, School of Software, , University of Technology Sydney, ; 2007 Sydney, Australia
                Article
                Publisher ID: 227
                DOI: 10.1007/s10278-019-00227-x
                PMC ID: 6646484
                PubMed ID: 31144149
                SO-VID: 9f10c8c2-2f78-4937-bef4-95d473814856
                Copyright © © The Author(s) 2019
                License:

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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                Subject: Article
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                issue-copyright-statement © Society for Imaging Informatics in Medicine 2019

                ScienceOpen disciplines: Radiology & Imaging
                Keywords: deep learning,medical image segmentation,cnn,organ segmentation
                Data availability:
                ScienceOpen disciplines: Radiology & Imaging
                Keywords: deep learning, medical image segmentation, cnn, organ segmentation

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