Texture Estimation for Abnormal Tissue Segmentation in Brain MRI

2016 ◽  
pp. 333-349 ◽  
Author(s):  
Syed M. S. Reza ◽  
Atiq Islam ◽  
Keyword(s):  
Brain Mri ◽  
Tissue Segmentation ◽  
Abnormal Tissue

Improving tissue segmentation of human brain MRI through preprocessing by the Gegenbauer reconstruction method

NeuroImage ◽  
2003 ◽  
Vol 20 (1) ◽  
pp. 489-502 ◽  
Author(s):  
Rick Archibald ◽  
Kewei Chen ◽  
Anne Gelb ◽  
Rosemary Renaut
Keyword(s):  
Human Brain ◽  
Brain Mri ◽  
Reconstruction Method ◽  
Tissue Segmentation ◽  
Gegenbauer Reconstruction

scholarly journals Anatomy‐guided joint tissue segmentation and topological correction for 6‐month infant brain MRI with risk of autism

2018 ◽  
Vol 39 (6) ◽  
pp. 2609-2623 ◽  
Author(s):  
Li Wang ◽  
Gang Li ◽  
Ehsan Adeli ◽  
Mingxia Liu ◽  
Zhengwang Wu ◽  
...  
Keyword(s):  
Brain Mri ◽  
Tissue Segmentation ◽  
Joint Tissue ◽  
Infant Brain

scholarly journals A Unified Framework for Brain Segmentation in MR Images

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
S. Yazdani ◽  
R. Yusof ◽  
A. Karimian ◽  
A. H. Riazi ◽  
M. Bennamoun
Keyword(s):  
Spatial Information ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Support Vector ◽  
Brain Segmentation ◽  
Combined Method ◽  
Unified Framework ◽  
Tissue Segmentation ◽  
Simulated Images

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets.

scholarly journals 3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images

2021 ◽  
Author(s):  
Zilong Zeng ◽  
Tengda Zhao ◽  
Lianglong Sun ◽  
Yihe Zhang ◽  
Mingrui Xia ◽  
...  
Keyword(s):  
Brain Mri ◽  
Ground Truth ◽  
Validation Dataset ◽  
Grand Challenge ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Convolutional Network ◽  
Infant Brain ◽  
Brain Mr Images ◽  
Segmentation Models

Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation model for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in limited samples of baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infant. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the largest improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

scholarly journals Automated Segmentation of Abnormal Tissues in Medical Images

2019 ◽  
Author(s):  
H Khastavaneh ◽  
H Ebrahimpour-komleh
Keyword(s):  
Diagnostic Procedures ◽  
Skin Lesions ◽  
Tissue Type ◽  
Automated Segmentation ◽  
Tissue Segmentation ◽  
Learning Techniques ◽  
Segmentation Methods ◽  
Specific Tissue ◽  
Better Than ◽  
Abnormal Tissue

Nowadays, medical image modalities are almost available everywhere. These modalities are bases of diagnosis of various diseases sensitive to specific tissue type. Usually physicians look for abnormalities in these modalities in diagnostic procedures. Count and volume of abnormalities are very important for optimal treatment of patients. Segmentation is a preliminary step for these measurements and also further analysis. Manual segmentation of abnormalities is cumbersome, error prone, and subjective. As a result, automated segmentation of abnormal tissue is a need. In this study, representative techniques for segmentation of abnormal tissues are reviewed. Main focus is on the segmentation of multiple sclerosis lesions, breast cancer masses, lung nodules, and skin lesions. As experimental results demonstrate, the methods based on deep learning techniques perform better than other methods that are usually based on handy feature engineering techniques. Finally, the most common measures to evaluate automated abnormal tissue segmentation methods are reported.

scholarly journals Harmonized Segmentation of Neonatal Brain MRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Irina Grigorescu ◽  
Lucy Vanes ◽  
Alena Uus ◽  
Dafnis Batalle ◽  
Lucilio Cordero-Grande ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Domain Adaptation ◽  
Brain Mri ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Imaging Data ◽  
Target Domain ◽  
Tissue Segmentation ◽  
Unsupervised Domain Adaptation ◽  
Preterm Born

Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on T2-weighted magnetic resonance imaging data of an unseen preterm-born neonatal population, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network and report our results in terms of Dice scores obtained on our source test dataset. Moreover, we analyse tissue volumes and cortical thickness measures of the harmonized data on a subset of the population matched for gestational age at birth and postmenstrual age at scan. Finally, we demonstrate the applicability of the harmonized cortical gray matter maps with an analysis comparing term and preterm-born neonates and a proof-of-principle investigation of the association between cortical thickness and a language outcome measure.

An Accurate Automated Local Similarity Factor-Based Neural Tree Approach toward Tissue Segmentation of Newborn Brain MRI

2018 ◽  
Vol 36 (11) ◽  
pp. 1157-1170 ◽  
Author(s):  
Tushar H. Jaware ◽  
K. B. Khanchandani ◽  
Anita Zurani
Keyword(s):  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Adult Brain ◽  
Local Similarity ◽  
Tissue Segmentation ◽  
Similarity Factor ◽  
Newborn Brain ◽  
Fully Automatic ◽  
Tree Approach ◽  
Brain Tissues

Background Segmentation of brain MR images of neonates is a primary step for assessment of brain evolvement. Advanced segmentation techniques used for adult brain MRI are not companionable for neonates, due to extensive dissimilarities in tissue properties and head structure. Existing segmentation methods for neonates utilizes brain atlases or requires manual elucidation, which results into improper and atlas dependent segmentation. Objective The primary objective of this work is to develop fully automatic, atlas free, and robust system to segment and classify brain tissues of newborn infants from magnetic resonance images. Study Design In this study, we propose a fully automatic, atlas-free pipeline based Neural Tree approach for segmentation of newborn brain MRI which utilizes resourceful local resemblance factor such as concerning, connectivity, structure, and relative tissue location. Physical collaboration and uses of an atlas are not required in proposed method and at the same time skirting atlas-associated bias which results in improved segmentation. Proposed technique segments and classify brain tissues both at global and tissue level. Results We examined our results through visual assessment by neonatologists and quantitative comparisons that show first-rate concurrence with proficient manual segmentations. The implementation results of the proposed technique provided a good overall accuracy of 91.82% for the segmentation of brain tissues as compared with other methods. Conclusion The pipelined-based neural tree approach along with local similarity factor segments and classify brain tissues. The proposed automated system have higher dice similarity coefficient as well as computational speed.

scholarly journals Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3363
Author(s):  
Chaitra Dayananda ◽  
Jae-Young Choi ◽  
Bumshik Lee
Keyword(s):  
Feature Extraction ◽  
Brain Tissue ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Similarity Index ◽  
Medical Image Segmentation ◽  
Model Parameters ◽  
Tissue Segmentation ◽  
Semantic Class ◽  
Multi Scale

In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.

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