Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks

A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing tumor core. The cascade is designed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the subregion hierarchy. The whole tumor is segmented in the first step and the bounding box of the result is used for the tumor core segmentation in the second step. The enhancing tumor core is then segmented based on the bounding box of the tumor core segmentation result. Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives. Residual connections and multi-scale predictions are employed in these networks to boost the segmentation performance. Experiments with BraTS 2017 validation set show that the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for enhancing tumor core, whole tumor and tumor core, respectively. The corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and 0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201

A vector machine based approach towards object oriented classification of remotely sensed imagery

Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. We have adopted certain automatic approaches based on advanced techniques such as Cellular Automata and Genetic Algorithm for improving effective overlap between classes. Proposed methodology has been evaluated in comparison with the conventional approaches with reference to the study area using relevant statistical parameters. Accuracy improvement of the proposed approach may be attributed to the effectiveness in combining spatial and spectral information

Алгоритм сегментації новоутворених пухлин на МРТ зображенні головного мозку за допомогою комбінацій нейронних мереж

Запропоновано алгоритм сегментації пухлин головного мозку на зображеннях МРТ, що реалізований на основі декількох ансамблів нейронних мереж. При ітерації алгоритму обчислення використовуються виходи базових нейронних мереж як вхідні данні для нової тренованої нейронної мережі, яка в подальшому виступає об’єднувачем для того, щоб відрізнити рубцеву тканину або не вражену тканину від клітин пухлини. Даний підхід має складний узагальнюючий характер, але, таким чином, вдається підвищити якість сегментації пухлини комбінацією нейронних мереж. Особливість алгоритму полягає в тому, що індивідуальний результат для кожного класифікатора визначається на основі натренованих раніше моделей, потім воксель класифікується як частина пухлини, якщо хоча б один з класифікаторів визначить його як пухлину. Далі, результат сегментації базових класифікаторів потрапляє на вхід вже навченого мета-класифікатора, який приймає остаточне рішення щодо приналежності вокселя на зображенні до клітин пухлини.The algorithm of segmentation of brain tumors in MRI images is proposed. In the iteration of the computation algorithm, the outputs of the base neural networks are used as input data for a new trained neural network, which in the future serves as a unifier in order to distinguish scar tissue or non-affected tissue from tumor cells. This approach has a complex generalization, but, thus, it is possible to improve the quality of segmentation of the tumor by a combination of neural networks. The components of the algorithm are basic classifiers that will extract complex functions of the regularities (often implicit) from the data stream, and the unifier will become a classifier that aggregates these functions. At the aggregation level, the data is derived from the classifiers, and the aggregation of the single output. When iterating the computation algorithm, the outputs of the basic classifiers are used as input data for the new trained neural network, which later acts as a unifier. The key idea of the algorithm is that the individual result for each classifier is determined based on the models previously trained, then the voxel is classified as part of the tumor if at least one of the classifiers determines it as a tumor. Further, the result of segmentation of the basic classifiers falls on the input of the already trained meta-classifier, which makes the final decision regarding the voxel's belonging to the image to the tumor cells. In this case, a special algorithm is used. The pixel algorithm proposes to classify pixels in adjacent areas based on gray levels. This method uses local information - the values of the gray levels of adjacent pixels, or, global information - the total distribution of the gray levels of adjacent pixels. The gray levels reflect the intensity of the light in each pixel. At the level of input data and manipulations with them there is an input to the input of the neural network for training.Предложен алгоритм сегментации опухолей головного мезга на изображениях МРТ, который реализован на основе нескольких ансамблів нейронних сетей. При интерации алгоритма вычисления используются выходы базових нейронних сетей как входные данные для новой тренированной нейронной сети, которая в дальнейшем выступает в качестве объединителя для того, чтобы от личить рубцовую ткань или не пораженную ткань от клеток опухоли. Данный поход имеет сложный обобщающий характер, но, таким образом, удается повысить качество сегментации опухоли комбинацией нейронних сетей. Особенность алгоритма заключается в том, что индивидуальный результат каждого классификатора определяется на основе натренированных ранее моделей, потом вексель классифицируется, как часть опухоли, если хоть один из классификаторов определяет его как опухоль. Далее результат сегментации базових классификаторов попадает на вход уже наученного мета-классификатора, который принимает окончательное решение по принадлежности векселя на изображении к клеткам опухоли

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

Discriminative Random Field Segmentation of Lung Nodules in CT Studies

The ability to conduct high-quality semiautomatic 3D segmentation of lung nodules in CT scans is of high value to busy radiologists. Discriminative random fields (DRFs) were used to segment 3D volumes of lung nodules in CT scan data using only one seed point per nodule. Optimal parameters for the DRF inference were first found using simulated annealing. These parameters were then used to solve the inference problem using the graph cuts algorithm. Results of the segmentation exhibited high precision and recall. The system can be adapted to facilitate the process of longitudinal studies but will still require human checking for failed cases

Chronology of brain tumor classification of intelligent systems based on mathematical modeling, simulation and image processing techniques

Tumor classification using image processing techniques is becoming a powerful tool nowadays. Based on the importance of this technique, the motivation of this review paper is to present the chronology of brain tumor classification using the digital images and govern the mathematical modeling and simulation of intelligent systems. The intelligent system involves artificial neural network (ANN), fuzzy logic (FL), support vector machine (SVM), and parallel support vector machine (PSVM). The chronology of brain tumor classification presents the latest part of the literature reviews related to the principal, type and interpretation of segmentation and classification of brain tumors via the large digital dataset from magnetic resonance imaging (MRI) images. This paper has been classified the modeling and simulation in classical and automatic models. Around 115 literature reviews in high ranking journal and high citation index are referred. This paper contains 6 contents, including mathematical modeling, numerical simulation, image processing, numerical results and performance, lastly is the conclusion to standardize the frame concept for the future of chronological framework involving the mathematical modeling and simulation. Research outcome to differentiate the tumor classification based on MRI images, modeling and simulation. Future work outlier in segmentation and classification are given in conclusion

Multi Modality Brain Mapping System (MBMS) Using Artificial Intelligence and Pattern Recognition

A Multimodality Brain Mapping System (MBMS), comprising one or more scopes (e.g., microscopes or endoscopes) coupled to one or more processors, wherein the one or more processors obtain training data from one or more first images and/or first data, wherein one or more abnormal regions and one or more normal regions are identified; receive a second image captured by one or more of the scopes at a later time than the one or more first images and/or first data and/or captured using a different imaging technique; and generate, using machine learning trained using the training data, one or more viewable indicators identifying one or abnormalities in the second image, wherein the one or more viewable indicators are generated in real time as the second image is formed. One or more of the scopes display the one or more viewable indicators on the second image

DEVELOPING MEDICAL IMAGE SEGMENTATION AND COMPUTER-AIDED DIAGNOSIS SYSTEMS USING DEEP NEURAL NETWORKS

Diagnostic medical imaging is an important non-invasive tool in medicine. It provides doctors (i.e., radiologists) with rich diagnostic information in clinical practice. Computer-aided diagnosis (CAD) schemes aim to provide a tool to assist the doctors for reading and interpreting medical images. Traditional CAD schemes are based on hand-crafted features and shallow supervised learning algorithms. They are greatly limited by the difficulties of accurate region segmentation and effective feature extraction. In this dissertation, our motivation is to apply deep learning techniques to address these challenges. We comprehensively investigated the feasibilities of applying deep learning technique to develop medical image segmentation and computer-aided diagnosis schemes for different imaging modalities and different tasks. First, we applied a two-step convolutional neural network architecture for selection of abdomen part and segmentation of subtypes of adipose tissue from abdominal CT images. We demonstrated high agreement between the segmentation generated by human and by our proposed deep learning models. Second, we explored to combine transfer learning technique with traditional hand-crafted features to improve the accuracy of breast mass classification from digital mammograms. Our results show that the ensemble of hand-crafted features and transferred features yields improvement of prediction performances. Third, we proposed a 3D fully convolutional network architecture with a novel coarse-to-fine residual module for prostate segmentation from MRI. State-of-art segmentation accuracy was obtained by using this model. We also investigated the feasibilities of applying fully convolutional network for prostate cancer detection based on multi-parametric MRI and obtained promising detection accuracy. Last, we proposed a novel cascaded neural network architecture with post-processing steps for nuclear segmentation from histology images. Superiority of the model was demonstrated by experiments. In summary, these study results demonstrated that deep learning is a very promising technology to help significantly improve efficacy of developing computer-aided diagnosis schemes of medical images and achieve higher performance