859 research outputs found
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low-and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource
3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context
We present an efficient deep learning approach for the challenging task of
tumor segmentation in multisequence MR images. In recent years, Convolutional
Neural Networks (CNN) have achieved state-of-the-art performances in a large
variety of recognition tasks in medical imaging. Because of the considerable
computational cost of CNNs, large volumes such as MRI are typically processed
by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D
patches. In this paper we introduce a CNN-based model which efficiently
combines the advantages of the short-range 3D context and the long-range 2D
context. To overcome the limitations of specific choices of neural network
architectures, we also propose to merge outputs of several cascaded 2D-3D
models by a voxelwise voting strategy. Furthermore, we propose a network
architecture in which the different MR sequences are processed by separate
subnetworks in order to be more robust to the problem of missing MR sequences.
Finally, a simple and efficient algorithm for training large CNN models is
introduced. We evaluate our method on the public benchmark of the BRATS 2017
challenge on the task of multiclass segmentation of malignant brain tumors. Our
method achieves good performances and produces accurate segmentations with
median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854
(enhancing core). Our approach can be naturally applied to various tasks
involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic
Deep Learning with Context Encoding for Semantic Brain Tumor Segmentation and Patient Survival Prediction
One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of natural scenes, with particular improvements in small object segmentation due to improved representative feature learning. Accordingly, we propose a novel, efficient 3DCNN based deep learning framework with context encoding for semantic brain tumor segmentation using multimodal magnetic resonance imaging (mMRI). The context encoding module in the proposed model enforces rich, class-dependent feature learning to improve the overall multi-label segmentation performance. We subsequently utilize context augmented features in a machine-learning based survival prediction pipeline to improve the prediction performance. The proposed method is evaluated using the publicly available 2019 Brain Tumor Segmentation (BraTS) and survival prediction challenge dataset. The results show that the proposed method significantly improves the tumor tissue segmentation performance and the overall survival prediction performance
Brain Tumor Segmentation Methods based on MRI images: Review Paper
Statistically, incidence rate of brain tumors for women is 26.55 per 100,000
and this rate for men is 22.37 per 100,000 on average. The most dangerous
occurring type of these tumors are known as Gliomas. The form of cancerous
tumors so-called Glioblastomas are so aggressive that patients between ages
40 to 64 have only a 5.3% chance with a 5-year survival rate. In addition, it
mostly depends on treatment course procedures since 331 to 529 is median
survival time that shows how this class is commonly severe form of brain
cancer. Unfortunately, a mean expenditure of glioblastoma costs 100,000$.
Due to high mortality rates, gliomas and glioblastomas should be determined
and diagnosed accurately to follow early stages of those cases. However, a
method which is suitable to diagnose a course of treatment and screen
deterministic features including location, spread and volume is multimodality
magnetic resonance imaging for gliomas. The tumor segmentation process is
determined through the ability to advance in computer vision. More precisely,
CNN (convolutional neural networks) demonstrates stable and effective
outcomes similar to other automated methods in terms of tumor segmentation
algorithms. However, I will present all methods separately to specify
effectiveness and accuracy of segmentation of tumor. Also, most commonly
known techniques based on GANs (generative adversarial networks) have an
advantage in some domains to analyze nature of manual segmentations.
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