Brain Tumor Image Segmentation Challenge Research Articles

Morphological and Marker-based Watershed Method for Detection and Segmentation of Brain Tumor Regions

Brain tumor detection is a complex problem in medical image analysis. Brain tumor is an abnormal growth of brain cellsthat is usually detected by Magnetic Resonance Images (MRI). In this paper, we propose an efficient algorithm for detecting brain tumors using MRI without skull removal. After applying basic image preprocessing techniques, morphological operations are used to detect the boundaries and sharpen the regions.Segmentation is performed using Otsu thresholding and then a marker watershed technique is used for final brain tumor segmentation. The proposed approach is evaluated on 3000 images from Brain Tumor Image Segmentation Challenge (BRATS) dataset. Experiments showed that with appropriate preprocessing and appropriate thresholding, good segmentation results can be achieved.

A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process

AbstractIn this paper, the tumor affected images are detected and classified from non‐tumor affected brain magnetic resonance imaging (MRI) using Adaptive Neuro Fuzzy Inference System (ANFIS) classification process. The proposed work for brain tumor detection consists of a noise reduction module, decomposition module, feature extraction module, classification module, and segmentation module. The noisy brain images are filtered by the proposed Vector Index Filtering algorithm and the filtered images are further decomposed using Fast Wavelet Transform. Later, the index features are computed from each decomposed sub‐band image and these computed index features are classified either as normal or abnormal brain images using the ANFIS classification process. Further, the tumor regions are segmented in classified abnormal brain images using morphological functions. This paper uses the Brain Tumor Image Segmentation Challenge—BRATS 2016 dataset for analyzing the performance of the proposed brain tumor detection and segmentation system in terms of sensitivity (97.9%), specificity (98.6%), accuracy (99.1%), error rate (0.09%), F1‐score (96.8%), and Geometrics mean (98.5%). Comparisons are made between the proposed method's experimental results and other state of the art methods. The proposed methods using machine learning approach, stated in this paper are well suited for detecting tumor regions in brain images.

Improving geometric P-norm-based glioma segmentation through deep convolutional autoencoder encapsulation

Gliomas are the most common types of brain tumors. Their shapes are irregular and their boundaries are ambiguous, producing a tumor that is hard to detect. To improve the segmentation performance, we propose a P-Norm-Based Glioma Segmentation by encapsulating it within a Deep Convolutional Autoencoder (DCA). In this paper, we first develop a new robust Deep P-norm Generative Model (DPGM) that can extract and classify the feature by the encoder section and segment this latter by the decoder section. After that, we utilise a Deep Discriminative Model (DDM) which applies the conditional random fields to refine the segmentation result. Effectiveness is gained by gradient descent training on the set of pre-P-norm filtering and thresolding convolutional operations using a novel geometric P-norm-based loss function. This latter will be optimized by the RANSAC layer, which is geared toward optimizing the strength of the correct model relative to the most convincing false model. We evaluate the accuracy and robustness of our method within MRI sequences (T1, T2, T2c & FLAIR) from the Multimodal Brain Tumor Image Segmentation Challenge (BRATS 2013 Leaderboard, BRATS 2013 Challenge, BRATS 2015 and BRATS 2017). The experiments demonstrate a high-quality segmentation results of our DPGM-DDM architecture and the average Dice is as high as 97.2%, 97.6%, and 98.4% respectively for complete, core, and enhancing tumor regions. Overall, our deep proposed architecture is effective in segmenting gliomas in multimodal images or fair images, and has the potential in routine examinations of gliomas in daily clinical practice.

3D brain glioma segmentation in MRI through integrating multiple densely connected 2D convolutional neural networks.

To overcome the computational burden of processing three-dimensional (3D) medical scans and the lack of spatial information in two-dimensional (2D) medical scans, a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2D convolutional neural networks (2D-CNNs). In order to combine the low-level features and high-level features, we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process. Further, in order to resolve the problems of the blurred boundary of the glioma edema area, we superimposed and fused the T2-weighted fluid-attenuated inversion recovery (FLAIR) modal image and the T2-weighted (T2) modal image to enhance the edema section. For the loss function of network training, we improved the cross-entropy loss function to effectively avoid network over-fitting. On the Multimodal Brain Tumor Image Segmentation Challenge (BraTS) datasets, our method achieves dice similarity coefficient values of 0.84, 0.82, and 0.83 on the BraTS2018 training; 0.82, 0.85, and 0.83 on the BraTS2018 validation; and 0.81, 0.78, and 0.83 on the BraTS2013 testing in terms of whole tumors, tumor cores, and enhancing cores, respectively. Experimental results showed that the proposed method achieved promising accuracy and fast processing, demonstrating good potential for clinical medicine.

Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation

AbstractCurrently, the analysis of magnetic resonance imaging (MRI) brain images of pathological patients is performed manually, both for the recognition of brain structures or lesions and for their characterization. Physicians sometimes encounter difficulties in interpreting these images for a reliable diagnosis of the patient's condition. This is due to the difficulty of detecting the nature of the lesions, particularly glioma. Glioma is one of the most common tumors, and one of the most difficult to detect because of its shape, irregularities, and ambiguous limits. The segmentation of these tumors is one of the most crucial steps for their classification and surgical planning. This article presents a new, accurate, and automatic approach for the precise segmentation of early gliomas (benign tumors), combining the random walk (RW) algorithm and the simple linear iterative clustering algorithm. The study was carried out in four steps. The first step consisted of decomposing the image into superpixels to obtain an initial outline of the tumor. The superpixels were generated using the SLIC algorithm. In the second step, for each superpixel, a set of statistical and multifractal characteristics were calculated (gray‐level co‐occurrence matrix, multifractal detrending moving average). In the third step, the superpixels were classified using a supervised random forest (RF) type classier into healthy or tumorous brain tissue. In the final step, the contour of the detected tumor was enhanced using the customized RW algorithm. The proposed method was evaluated using the Brain Tumor Image Segmentation Challenge 2013 database. The results obtained are competitive compared to other existing methods.

A BrainNet Classification Technique Based on Deep Convolutional Neural Network for Detection of Brain Tumor in FLAIR MRI Images

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. The logical gap between the visual representation of data captured by MRI device and the information apparent to the person evaluating poses a key challenge in the medical field. Research in computerized segmentation of tumor is widely gaining popularity nowadays, which may lead to an accurate analysis of MRI images and planned treatment of patients. The recent field of deep learning and neural networks promises to classify images with higher accuracy. This work proposes a new BrainNet classification technique that combines fuzzy c means, morphological operators and CNN to identify image regions that are suspicious. The proposed method is assessed with the help of imaging data obtained from Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and IXI dataset. The effectiveness of the proposed method is computed with traditional machine learning and Convolutional Neural Networks. Experimental results show that our proposed method outperforms state-of-the-art classification on the BRATS 2015 dataset.

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provides doctors with meaningful and reliable quantitative information in diagnosing and monitoring neurological diseases. Successful deep learning-based proposals have been designed, and most of them are built upon image patches. In this paper, a novel end-to-end brain tumor segmentation method is developed using an improved fully convolutional network by modifying the U-Net architecture. In our network, an innovative structure referred to as an up skip connection is first proposed between the encoding path and decoding path to enhance information flow. Moreover, an inception module is adopted in each block to help our network learn richer representations, and an efficient cascade training strategy is introduced to segment brain tumor subregions sequentially. In contrast to those patchwise methods, our model can automatically generate segmentation maps slice by slice. We have validated our proposal by using imaging data from the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and BRATS 2016. Experimental results compared with U-Net suggest that our method is 2.6%, 3.9%, and 5.2% higher (by using the BRATS 2015 training dataset) as well as 2.8%, 3.7%, and 8.1% (by using the BRATS 2017 training dataset) higher in terms of complete, core and enhancing tumor regions, respectively. Quantitative and visual evaluation of our method has revealed the effectiveness of the proposed improvements and indicated that our end-to-end segmentation method can achieve a performance that can compete with state-of-the-art brain tumor segmentation approaches.

Fusion based Glioma brain tumor detection and segmentation using ANFIS classification

The detection of tumor regions in Glioma brain image is a challenging task due to its low sensitive boundary pixels. In this paper, Non-Sub sampled Contourlet Transform (NSCT) is used to enhance the brain image and then texture features are extracted from the enhanced brain image. These extracted features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) approach to classify the brain image into normal and Glioma brain image. Then, the tumor regions in Glioma brain image is segmented using morphological functions. The proposed Glioma brain tumor detection methodology is applied on the Brain Tumor image Segmentation challenge (BRATS) open access dataset in order to evaluate the performance.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Automatic metastatic brain tumor segmentation for stereotactic radiosurgery applications

The objective of this study is to develop an automatic segmentation strategy for efficient and accurate metastatic brain tumor delineation on contrast-enhanced T1-weighted (T1c) magnetic resonance images (MRI) for stereotactic radiosurgery (SRS) applications. The proposed four-step automatic brain metastases segmentation strategy is comprised of pre-processing, initial contouring, contour evolution, and contour triage. First, T1c brain images are preprocessed to remove the skull. Second, an initial tumor contour is created using a multi-scaled adaptive threshold-based bounding box and a super-voxel clustering technique. Third, the initial contours are evolved to the tumor boundary using a regional active contour technique. Fourth, all detected false-positive contours are removed with geometric characterization. The segmentation process was validated on a realistic virtual phantom containing Gaussian or Rician noise. For each type of noise distribution, five different noise levels were tested. Twenty-one cases from the multimodal brain tumor image segmentation (BRATS) challenge dataset and fifteen clinical metastases cases were also included in validation. Segmentation performance was quantified by the Dice coefficient (DC), normalized mutual information (NMI), structural similarity (SSIM), Hausdorff distance (HD), mean value of surface-to-surface distance (MSSD) and standard deviation of surface-to-surface distance (SDSSD). In the numerical phantom study, the evaluation yielded a DC of 0.98 ± 0.01, an NMI of 0.97 ± 0.01, an SSIM of 0.999 ± 0.001, an HD of 2.2 ± 0.8 mm, an MSSD of 0.1 ± 0.1 mm, and an SDSSD of 0.3 ± 0.1 mm. The validation on the BRATS data resulted in a DC of 0.89 ± 0.08, which outperform the BRATS challenge algorithms. Evaluation on clinical datasets gave a DC of 0.86 ± 0.09, an NMI of 0.80 ± 0.11, an SSIM of 0.999 ± 0.001, an HD of 8.8 ± 12.6 mm, an MSSD of 1.5 ± 3.2 mm, and an SDSSD of 1.8 ± 3.4 mm when comparing to the physician drawn ground truth. The result indicated that the developed automatic segmentation strategy yielded accurate brain tumor delineation and presented as a useful clinical tool for SRS applications.

Extended Modality Propagation: Image Synthesis of Pathological Cases.

This paper describes a novel generative model for the synthesis of multi-modal medical images of pathological cases based on a single label map. Our model builds upon i) a generative model commonly used for label fusion and multi-atlas patch-based segmentation of healthy anatomical structures, ii) the Modality Propagation iterative strategy used for a spatially-coherent synthesis of subject-specific scans of desired image modalities. The expression Extended Modality Propagation is coined to refer to the extension of Modality Propagation to the synthesis of images of pathological cases. Moreover, image synthesis uncertainty is estimated. An application to Magnetic Resonance Imaging synthesis of glioma-bearing brains is i) validated on the training dataset of a Multimodal Brain Tumor Image Segmentation challenge, ii) compared to the state-of-the-art in glioma image synthesis, and iii) illustrated using the output of two different tumor growth models. Such a generative model allows the generation of a large dataset of synthetic cases, which could prove useful for the training, validation, or benchmarking of image processing algorithms.

SU‐C‐BRA‐06: Automatic Brain Tumor Segmentation for Stereotactic Radiosurgery Applications

Purpose:Stereotactic radiosurgery (SRS), which delivers a potent dose of highly conformal radiation to the target in a single fraction, requires accurate tumor delineation for treatment planning. We present an automatic segmentation strategy, that synergizes intensity histogram thresholding, super‐voxel clustering, and level‐set based contour evolving methods to efficiently and accurately delineate SRS brain tumors on contrast‐enhance T1‐weighted (T1c) Magnetic Resonance Images (MRI).Methods:The developed auto‐segmentation strategy consists of three major steps. Firstly, tumor sites are localized through 2D slice intensity histogram scanning. Then, super voxels are obtained through clustering the corresponding voxels in 3D with reference to the similarity metrics composited from spatial distance and intensity difference. The combination of the above two could generate the initial contour surface. Finally, a localized region active contour model is utilized to evolve the surface to achieve the accurate delineation of the tumors. The developed method was evaluated on numerical phantom data, synthetic BRATS (Multimodal Brain Tumor Image Segmentation challenge) data, and clinical patients’ data. The auto‐segmentation results were quantitatively evaluated by comparing to ground truths with both volume and surface similarity metrics.Results:DICE coefficient (DC) was performed as a quantitative metric to evaluate the auto‐segmentation in the numerical phantom with 8 tumors. DCs are 0.999±0.001 without noise, 0.969±0.065 with Rician noise and 0.976±0.038 with Gaussian noise. DC, NMI (Normalized Mutual Information), SSIM (Structural Similarity) and Hausdorff distance (HD) were calculated as the metrics for the BRATS and patients’ data. Assessment of BRATS data across 25 tumor segmentation yield DC 0.886±0.078, NMI 0.817±0.108, SSIM 0.997±0.002, and HD 6.483±4.079mm. Evaluation on 8 patients with total 14 tumor sites yield DC 0.872±0.070, NMI 0.824±0.078, SSIM 0.999±0.001, and HD 5.926±6.141mm.Conclusion:The developed automatic segmentation strategy, which yields accurate brain tumor delineation in evaluation cases, is promising for its application in SRS treatment planning.

A Patch-Based Approach for the Segmentation of Pathologies: Application to Glioma Labelling.

In this paper, we describe a novel and generic approach to address fully-automatic segmentation of brain tumors by using multi-atlas patch-based voting techniques. In addition to avoiding the local search window assumption, the conventional patch-based framework is enhanced through several simple procedures: an improvement of the training dataset in terms of both label purity and intensity statistics, augmented features to implicitly guide the nearest-neighbor-search, multi-scale patches, invariance to cube isometries, stratification of the votes with respect to cases and labels. A probabilistic model automatically delineates regions of interest enclosing high-probability tumor volumes, which allows the algorithm to achieve highly competitive running time despite minimal processing power and resources. This method was evaluated on Multimodal Brain Tumor Image Segmentation challenge datasets. State-of-the-art results are achieved, with a limited learning stage thus restricting the risk of overfit. Moreover, segmentation smoothness does not involve any post-processing.