Parallel Capsule Net for Ischemic Stroke Segmentation

2019 ◽  
Author(s):  
MD Sharique ◽  
Bondi Uday Pundarikaksha ◽  
Pradeeba Sridar ◽  
R S Rama Krishnan ◽  
Ramarathnam Krishnakumar
Keyword(s):  
Image Segmentation ◽  
Ischemic Stroke ◽  
Spatial Information ◽  
Automatic Segmentation ◽  
Class Imbalance ◽  
Medical Image Segmentation ◽  
Brain Images ◽  
Data Set ◽  
Max Pooling ◽  
Mri Brain Images

AbstractStroke is one of the leading causes of disability. Segmentation of ischemic stroke could help in planning an optimal treatment. Currently, radiologists use manual segmentation, which can often be time-consuming, laborious and error-prone. Automatic segmentation of ischemic stroke in MRI brain images is a challenging problem due to its small size, multiple occurrences and the need to use multiple image modalities. In this paper, we propose a new architecture for image segmentation, called Parallel Capsule Net, which uses max pooling in every parallel pathways along with dense connections between the parallel layers. We hypothesise that the spatial information lost due to max pooling in these layers can be retrieved by the use of such dense connections. In order to combine the information encoded by the parallel layers, outputs of the layers are concatenated before upsampling. We also propose the use of a modified loss function which consists of a regional term (Generalized Dice loss + Focal Loss) and a boundary term (Boundary loss) to address the problem of class imbalance which is prevalent in medical images. We achieved a competitive Dice score of 0.754, on ISLES SISS data set, compared to a score of 0.67 reported in earlier studies. We also obtained a Dice score of 0.902 with another popular data set, ATLAS. The proposed parallel capsule net can be extended to other similar medical image segmentation problems.

An Extended Fuzzy C-Means Segmentation for an Efficient BTD With the Region of Interest of SCP

2021 ◽  
Vol 12 (4) ◽  
pp. 11-24
Author(s):  
Subba Reddy K. ◽  
Rajendra Prasad K.
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Region Of Interest ◽  
Tumor Segmentation ◽  
Brain Images ◽  
Brain Image ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Irregular Boundaries ◽  
Mri Brain Images

Magnetic resonance imaging (MRI) is the primary source to diagnose a brain tumor or masses in the medical sciences. It is emerging to detect the tumors from the scanned MRI brain images at early stages for the best treatments. Existing image segmentation techniques, morphological, fuzzy c-means are wildly successful in the extraction region of interest (ROI) in brain image segmentation. Proper extraction of ROIs is useful for regularizing the regions of tumors from the brain image with effective binarization in the segmentation. However, the existing techniques are limiting the irregular boundaries or shapes in tumor segmentation. Thus, this paper presents the proposed work extending the FCM with the spatial correlated pixel (RSCP), known as FCM-RSCP. It overcomes the problem of irregular boundaries by assessing correlated spatial information during segmentation. Benchmarked MRI brain images are used in the experiment for demonstrating the efficiency of the proposed methodology.

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

Medical Imaging Importance in the Real World

2020 ◽  
pp. 136-162
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Level Set ◽  
Real World ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Image Resolution ◽  
Variational Model ◽  
Medical Applications

In the medical image resolution, automatic segmentation is a challenging task, and it's still an unsolved problem for most medical applications due to the wide variety connected with image modalities, encoding parameters, and organic variability. In this chapter, a review and critique of medical image segmentation using clustering, compression, histogram, edge detection, parametric, variational model. and level set-based methods is presented. Modes of segmentation like manual, semi-automatic, interactive, and automatic are also discussed. To present current challenges, aim and motivation for doing fast, interactive and correct segmentation, the medical image modalities X-ray, CT, MRI, and PET are discussed in this chapter.

The Image Segmentation System Based on Gauss Process

2013 ◽  
Vol 421 ◽  
pp. 523-527
Author(s):  
Lei Ling ◽  
Pan Chen ◽  
Li Ping
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Target Position ◽  
Training Sample ◽  
Target Object ◽  
Medical Image Segmentation ◽  
Training Samples ◽  
Gauss Process ◽  
Segmentation Image ◽  
Valid Information

This paper gave an example for the design of automatic image segmentation system by using deep staining of blood cell image. The paper also described how to auto-locate the target position, and how to collect training samples with large entropy further. The spatial information of target object also contained valid information, so this paper put forward to use the relative distance between the inner points and the centre of a circle as a feature of a training sample to work together with the RGB features. And for the segmentation image can be applied to the later medical diagnosis conveniently, the Gauss process classifier had been used in medical image segmentation firstly because of its clear probabilistic interpretation. Compared with SVM, GP is better in this system.

scholarly journals An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ningning Zhou ◽  
Tingting Yang ◽  
Shaobai Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Fuzzy Information ◽  
Spatial Features ◽  
Image Segmentation Algorithm ◽  
Truth Degree

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

Sign in / Sign up

Export Citation Format

Share Document