P04.06 Can BraTS Toolkit Brain Tumor Segmentation Algorithms be relevant when dealing with post-operative MRI in clinical practice?

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii19-ii20
Author(s):  
L Piram ◽  
C Acquitter ◽  
U Sabatini ◽  
B Lemasson ◽  
E Moyal ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Hausdorff Distance ◽  
De Novo ◽  
Automatic Segmentation ◽  
Outcome Evaluation ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Image Conversion

Abstract BACKGROUND Segmentation of brain tumors is crucial for radiotherapy plans and treatment outcome evaluation. As manual segmentation (MS) is a time-consuming task, many algorithms to automate the process were proposed over the past decades. BraTS toolkit (BTK) offers a solution for automatic brain tumor segmentation in 3 steps: a Preprocessor for image conversion and registration, a Segmentor generating segmentations from 4 deep-learning based algorithms and a Fusionator combining the results. As most algorithms published, BTK was trained on pre-operative data. Yet, as surgery is the first treatment of glioblastomas, most MRIs in clinical practice are post-operative images. This study aimed to assess whether segmentation of post-operative brain tumors could benefit from an initial automatic segmentation (AS) using BTK. MATERIAL AND METHODS MRI dataset from the multicenter, STERIMGLI clinical trial provided 92 series from 25 patients with a unifocal recurrence of glioblastoma. AS were generated using BTK Preprocessor and Segmentor. Out of the 4 algorithms output, the best AS was selected after visual appraisal. AS contained 3 labels: T1w contrast enhanced tumor (ET), flair edema (ED) and non-enhanced tumor (NET). AS were then reviewed by a radiation oncologist and a neuroradiologist to produce the MS. ET and ED were corrected; surgical cavity (SC) was also segmented, either from the NET label or de novo. Dice-score, Hausdorff Distance (HD) and Average Hausdorff Distance (AHD) were used to quantify the similarity between AS and MS for each label. RESULTS AS succeeded to label 89.3% (82/92), 100% (92/92) and 85.8% (79/92) of ET, ED and NET respectively. Among the 4 algorithms in BTK, Zyx_2019 produced 36% of AS, mic-dkfz 32%, xfeng 18%, lfb_rwth 14%. Mean Dice-scores of 75.8%, 94.8% were found for ET and ED respectively. Mean HD and AHD were 25.2mm (±39.7), 2.8mm (±10.9) for ET; 14.9mm (±25.8), 0.9mm (±5.8) for ED. Concerning SC, Dice-scores were <0.1 for 49% (39/79), >0.6 for 30% (42/79). CONCLUSION BraTS Toolkit was trained to segment necrosis as the label NET. Still, it detected the surgical cavity and saved time for the MS in 51% of post-operative cases. Even though BTK was designed to segment pre-operative brain tumors, similarity metrics show that minimal or no manual corrections are necessary most of the time when used to segment ET and ED on post-operative MRI acquired in clinical routine. The development of a unique AS tool for pre and post-operative images would be useful in clinical practice.

scholarly journals MRI Modality-based Brain Tumor Segmentation Using Deep Neural Networks

2021 ◽  
Author(s):  
Pankaj Eknath Kasar ◽  
Shivajirao M. Jadhav ◽  
Vineet Kansal
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Clinical Practice ◽  
Brain Tumors ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation

Abstract The tumor detection is major challenging task in brain tumor quantitative evaluation. In recent years, owing to non-invasive and strong soft tissue comparison, Magnetic Resonance Imaging (MRI) has gained great interest. MRI is a commonly used image modality technique to locate brain tumors. An immense amount of data is produced by the MRI. Heterogeneity, isointense and hypointense tumor properties restrict manual segmentation in a fair period of time, thus restricting the use of reliable quantitative measures in clinical practice. In the clinical practice manual segmentation task is quite time consuming and their performance is highly depended on the operator’s experience. Accurate and automated tumor segmentation techniques are also needed; however, the severe spatial and structural heterogeneity of brain tumors makes automatic segmentation a difficult job. This paper proposes fully automatic segmentation of brain tumors using encoder-decoder based convolutional neural networks. The paper focuses on well-known semantic segmentation deep neural networks i.e., UNET and SEGNET for segmenting tumors from Brain MRI images. The networks are trained and tested using freely accessible standard dataset, with Dice Similarity Coefficient (DSC) as metric for whole predicted image i.e., including tumor and background. UNET’s average DSC on test dataset is 0.76 whereas for SEGNET we got average DSC 0.67. The evaluation of results proves that UNET is having better performance than SEGNET.

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences

2022 ◽  
Vol 22 (1) ◽  
pp. 1-30
Author(s):  
Rahul Kumar ◽  
Ankur Gupta ◽  
Harkirat Singh Arora ◽  
Balasubramanian Raman
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Tumor Classification ◽  
Training Dataset ◽  
Tumor Segmentation ◽  
Discrete Wavelet ◽  
Low Grade ◽  
Brain Tumor Segmentation ◽  
Mri Sequences

Brain tumors are one of the critical malignant neurological cancers with the highest number of deaths and injuries worldwide. They are categorized into two major classes, high-grade glioma (HGG) and low-grade glioma (LGG), with HGG being more aggressive and malignant, whereas LGG tumors are less aggressive, but if left untreated, they get converted to HGG. Thus, the classification of brain tumors into the corresponding grade is a crucial task, especially for making decisions related to treatment. Motivated by the importance of such critical threats to humans, we propose a novel framework for brain tumor classification using discrete wavelet transform-based fusion of MRI sequences and Radiomics feature extraction. We utilized the Brain Tumor Segmentation 2018 challenge training dataset for the performance evaluation of our approach, and we extract features from three regions of interest derived using a combination of several tumor regions. We used wrapper method-based feature selection techniques for selecting a significant set of features and utilize various machine learning classifiers, Random Forest, Decision Tree, and Extra Randomized Tree for training the model. For proper validation of our approach, we adopt the five-fold cross-validation technique. We achieved state-of-the-art performance considering several performance metrics, 〈 Acc , Sens , Spec , F1-score , MCC , AUC 〉 ≡ 〈 98.60%, 99.05%, 97.33%, 99.05%, 96.42%, 98.19% 〉, where Acc , Sens , Spec , F1-score , MCC , and AUC represents the accuracy, sensitivity, specificity, F1-score, Matthews correlation coefficient, and area-under-the-curve, respectively. We believe our proposed approach will play a crucial role in the planning of clinical treatment and guidelines before surgery.

scholarly journals A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction

2021 ◽  
Author(s):  
Rupal Agravat ◽  
Mehul Raval
Keyword(s):  
Overall Survival ◽  
Brain Tumor ◽  
Automatic Segmentation ◽  
High Volume ◽  
Physical Symptoms ◽  
Tumor Segmentation ◽  
Survival Prediction ◽  
Neural Network Models ◽  
Brain Tumor Segmentation ◽  
Overall Survival Prediction

<div>Glioma is the most deadly brain tumor with high mortality. Treatment planning by human experts depends on the proper diagnosis of physical symptoms along with Magnetic Resonance(MR) image analysis. Highly variability of a brain tumor in terms of size, shape, location, and a high volume of MR images makes the analysis time-consuming. Automatic segmentation methods achieve a reduction in time with excellent reproducible results.</div><div>The article aims to survey the advancement of automated methods for Glioma brain tumor segmentation. It is also essential to make an objective evaluation of various models based on the benchmark. Therefore, the 2012 - 2019 BraTS challenges database evaluates state-of-the-art methods. The complexity of tasks under the challenge has grown from segmentation (Task1) to overall survival prediction (Task 2) to uncertainty prediction for classification (Task 3). The paper covers the complete gamut of brain tumor segmentation using handcrafted features to deep neural network models for Task 1. The aim is to showcase a complete change of trends in automated brain tumor models. The paper also covers end to end joint models involving brain tumor segmentation and overall survival prediction. All the methods are probed, and parameters that affect performance are tabulated and analyzed.</div>

scholarly journals Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 2051-2054
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Important Task ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Medical Issues ◽  
Current Scenario ◽  
Exact Position ◽  
The Brain

Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

2021 ◽  
Vol 30 (06n08) ◽  
Author(s):  
V. K. Deepak ◽  
R. Sarath
Keyword(s):  
Brain Tumor ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Grey Wolf ◽  
Dice Coefficient ◽  
Grey Wolf Optimization ◽  
Brain Tumor Segmentation ◽  
Better Than

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Brain Tumor Segmentation via Multi-Modalities Interactive Feature Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Bo Wang ◽  
Jingyi Yang ◽  
Hong Peng ◽  
Jingyang Ai ◽  
Lihua An ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Feature Learning ◽  
Image Data ◽  
Brain Regions ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Brain Tumor Segmentation ◽  
Learning Framework ◽  
Interactive Feature

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions. In this paper, we propose a novel cross-modalities interactive feature learning framework to segment brain tumors from the multi-modalities data. The core idea is that the multi-modality MR data contain rich patterns of the normal brain regions, which can be easily captured and can be potentially used to detect the non-normal brain regions, i.e., brain tumor regions. The proposed multi-modalities interactive feature learning framework consists of two modules: cross-modality feature extracting module and attention guided feature fusing module, which aim at exploring the rich patterns cross multi-modalities and guiding the interacting and the fusing process for the rich features from different modalities. Comprehensive experiments are conducted on the BraTS 2018 benchmark, which show that the proposed cross-modality feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.

scholarly journals Development of Deep Learning Algorithm for Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 2800-2803
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Human Errors ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

Sign in / Sign up

Export Citation Format

Share Document