scholarly journals ITK Tutorial Presentation Slides

2006 ◽  
Author(s):  
Luis Ibanez ◽  
Lydia Ng ◽  
Josh Cates ◽  
Stephen Aylward ◽  
Bill Lorensen ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Open Source ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Data Representation ◽  
Medical Image Segmentation ◽  
Object Oriented Programming ◽  
Application Development

This course introduces attendees to select open-source efforts in the field of medical image analysis. Opportunities for users and developers are presented. The course particularly focuses on the open-source Insight Toolkit (ITK) for medical image segmentation and registration. The course describes the procedure for downloading and installing the toolkit and covers the use of its data representation and filtering classes. Attendees are shown how ITK can be used in their research, rapid prototyping, and application development.LEARNING OUTCOMES After completing this course, attendees will be able to: contribute to and benefit from open-source software for medical image analysis download and install the ITK toolkit start their own software project based on ITK design and construct an image processing pipeline combine ITK filters for medical image segmentation combine ITK components for medical image registrationINTENDED AUDIENCE This course is intended for anyone involved in medical image analysis. In particular it targets graduate students, researchers and professionals in the areas of computer science and medicine. Attendees should have an intermediate level on object oriented programming with C++ and must be familiar with the basics of medical image processing and analysis.

scholarly journals Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 844
Author(s):  
Baixin Jin ◽  
Pingping Liu ◽  
Peng Wang ◽  
Lida Shi ◽  
Jing Zhao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
Optic Disc ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Attention Mechanism ◽  
Cross Entropy ◽  
Segmentation Task

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.

Medical Image Segmentation based on U-Net: A Review

2020 ◽  
Vol 64 (2) ◽  
pp. 20508-1-20508-12 ◽  
Author(s):  
Getao Du ◽  
Xu Cao ◽  
Jimin Liang ◽  
Xueli Chen ◽  
Yonghua Zhan
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Imaging Systems

Abstract Medical image analysis is performed by analyzing images obtained by medical imaging systems to solve clinical problems. The purpose is to extract effective information and improve the level of clinical diagnosis. In recent years, automatic segmentation based on deep learning (DL) methods has been widely used, where a neural network can automatically learn image features, which is in sharp contrast with the traditional manual learning method. U-net is one of the most important semantic segmentation frameworks for a convolutional neural network (CNN). It is widely used in the medical image analysis domain for lesion segmentation, anatomical segmentation, and classification. The advantage of this network framework is that it can not only accurately segment the desired feature target and effectively process and objectively evaluate medical images but also help to improve accuracy in the diagnosis by medical images. Therefore, this article presents a literature review of medical image segmentation based on U-net, focusing on the successful segmentation experience of U-net for different lesion regions in six medical imaging systems. Along with the latest advances in DL, this article introduces the method of combining the original U-net architecture with deep learning and a method for improving the U-net network.

scholarly journals Match Feature U-Net: Dynamic Receptive Field Networks for Biomedical Image Segmentation

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1230
Author(s):  
Xiaofei Qin ◽  
Chengzi Wu ◽  
Hang Chang ◽  
Hao Lu ◽  
Xuedian Zhang
Keyword(s):  
Image Segmentation ◽  
Receptive Field ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Cell Segmentation ◽  
Biomedical Image ◽  
Nuclei Segmentation ◽  
Kernel Convolution

Medical image segmentation is a fundamental task in medical image analysis. Dynamic receptive field is very helpful for accurate medical image segmentation, which needs to be further studied and utilized. In this paper, we propose Match Feature U-Net, a novel, symmetric encoder– decoder architecture with dynamic receptive field for medical image segmentation. We modify the Selective Kernel convolution (a module proposed in Selective Kernel Networks) by inserting a newly proposed Match operation, which makes similar features in different convolution branches have corresponding positions, and then we replace the U-Net’s convolution with the redesigned Selective Kernel convolution. This network is a combination of U-Net and improved Selective Kernel convolution. It inherits the advantages of simple structure and low parameter complexity of U-Net, and enhances the efficiency of dynamic receptive field in Selective Kernel convolution, making it an ideal model for medical image segmentation tasks which often have small training data and large changes in targets size. Compared with state-of-the-art segmentation methods, the number of parameters in Match Feature U-Net (2.65 M) is 34% of U-Net (7.76 M), 29% of UNet++ (9.04 M), and 9.1% of CE-Net (29 M). We evaluated the proposed architecture in four medical image segmentation tasks: nuclei segmentation in microscopy images, breast cancer cell segmentation, gland segmentation in colon histology images, and disc/cup segmentation. Our experimental results show that Match Feature U-Net achieves an average Mean Intersection over Union (MIoU) gain of 1.8, 1.45, and 2.82 points over U-Net, UNet++, and CE-Net, respectively.

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

Review on 2D and 3D MRI Image Segmentation Techniques

2019 ◽  
Vol 15 (2) ◽  
pp. 150-160 ◽  
Author(s):  
S. Shirly ◽  
K. Ramesh
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Diagnostics ◽  
Processing Technique ◽  
Image Features ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Edge Based ◽  
Mri Image

Background: Magnetic Resonance Imaging is most widely used for early diagnosis of abnormalities in human organs. Due to the technical advancement in digital image processing, automatic computer aided medical image segmentation has been widely used in medical diagnostics. </P><P> Discussion: Image segmentation is an image processing technique which is used for extracting image features, searching and mining the medical image records for better and accurate medical diagnostics. Commonly used segmentation techniques are threshold based image segmentation, clustering based image segmentation, edge based image segmentation, region based image segmentation, atlas based image segmentation, and artificial neural network based image segmentation. Conclusion: This survey aims at providing an insight about different 2-Dimensional and 3- Dimensional MRI image segmentation techniques and to facilitate better understanding to the people who are new in this field. This comparative study summarizes the benefits and limitations of various segmentation techniques.

scholarly journals THE LAYERED NET SURFACE PROBLEMS IN DISCRETE GEOMETRY AND MEDICAL IMAGE SEGMENTATION

2007 ◽  
Vol 17 (03) ◽  
pp. 261-296 ◽  
Author(s):  
XIAODONG WU ◽  
DANNY Z. CHEN ◽  
KANG LI ◽  
MILAN SONKA
Keyword(s):  
Image Segmentation ◽  
Polynomial Time ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Simultaneous Detection ◽  
Exact Algorithms ◽  
Medical Image Segmentation ◽  
Efficient Detection ◽  
Column Graph ◽  
Voxel Grid

Efficient detection of multiple inter-related surfaces representing the boundaries of objects of interest in d-D images (d ≥ 3) is important and remains challenging in many medical image analysis applications. In this paper, we study several layered net surface (LNS) problems captured by an interesting type of geometric graphs called ordered multi-column graphs in the d-D discrete space (d ≥ 3 is any constant integer). The LNS problems model the simultaneous detection of multiple mutually related surfaces in three or higher dimensional medical images. Although we prove that the d-D LNS problem (d ≥ 3) on a general ordered multi-column graph is NP-hard, the (special) ordered multi-column graphs that model medical image segmentation have the self-closure structures and thus admit polynomial time exact algorithms for solving the LNS problems. Our techniques also solve the related net surface volume (NSV) problems of computing well-shaped geometric regions of an optimal total volume in a d-D weighted voxel grid. The NSV problems find applications in medical image segmentation and data mining. Our techniques yield the first polynomial time exact algorithms for several high dimensional medical image segmentation problems. Experiments and comparisons based on real medical data showed that our LNS algorithms and software are computationally efficient and produce highly accurate and consistent segmentation results.

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