5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Related collections

          Most cited references 16

          • Record: found
          • Abstract: found
          • Article: not found

          Region-Based Convolutional Networks for Accurate Object Detection and Segmentation.

          Object detection performance, as measured on the canonical PASCAL VOC Challenge datasets, plateaued in the final years of the competition. The best-performing methods were complex ensemble systems that typically combined multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 50 percent relative to the previous best result on VOC 2012-achieving a mAP of 62.4 percent. Our approach combines two ideas: (1) one can apply high-capacity convolutional networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data are scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, boosts performance significantly. Since we combine region proposals with CNNs, we call the resulting model an R-CNN or Region-based Convolutional Network. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Deep convolutional neural networks for multi-modality isointense infant brain image segmentation.

              The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6-8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multi-modality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement.
                Bookmark

                Author and article information

                Contributors
                +61 2 95147873 , mh.hesamian@gmail.com
                Journal
                J Digit Imaging
                J Digit Imaging
                Journal of Digital Imaging
                Springer International Publishing (Cham )
                0897-1889
                1618-727X
                29 May 2019
                29 May 2019
                August 2019
                : 32
                : 4
                : 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
                227
                10.1007/s10278-019-00227-x
                6646484
                31144149
                © The Author(s) 2019

                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.

                Categories
                Article
                Custom metadata
                © Society for Imaging Informatics in Medicine 2019

                Radiology & Imaging

                organ segmentation, cnn, medical image segmentation, deep learning

                Comments

                Comment on this article