11,440 research outputs found
Recommended from our members
Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information.
The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Toward this, we use an existing brain parcellation atlas in the Montreal Neurological Institute (MNI) space and map this atlas to the individual subject data. This mapped atlas in the subject data space is integrated with structural Magnetic Resonance (MR) imaging data, and patch-based neural networks, including 3D U-Net and DeepMedic, are trained to classify the different brain lesions. Multiple state-of-the-art neural networks are trained and integrated with XGBoost fusion in the proposed two-level ensemble method. The first level reduces the uncertainty of the same type of models with different seed initializations, and the second level leverages the advantages of different types of neural network models. The proposed location information fusion method improves the segmentation performance of state-of-the-art networks including 3D U-Net and DeepMedic. Our proposed ensemble also achieves better segmentation performance compared to the state-of-the-art networks in BraTS 2017 and rivals state-of-the-art networks in BraTS 2018. Detailed results are provided on the public multimodal brain tumor segmentation (BraTS) benchmarks
TBI Contusion Segmentation from MRI using Convolutional Neural Networks
Traumatic brain injury (TBI) is caused by a sudden trauma to the head that
may result in hematomas and contusions and can lead to stroke or chronic
disability. An accurate quantification of the lesion volumes and their
locations is essential to understand the pathophysiology of TBI and its
progression. In this paper, we propose a fully convolutional neural network
(CNN) model to segment contusions and lesions from brain magnetic resonance
(MR) images of patients with TBI. The CNN architecture proposed here was based
on a state of the art CNN architecture from Google, called Inception. Using a
3-layer Inception network, lesions are segmented from multi-contrast MR images.
When compared with two recent TBI lesion segmentation methods, one based on CNN
(called DeepMedic) and another based on random forests, the proposed algorithm
showed improved segmentation accuracy on images of 18 patients with mild to
severe TBI. Using a leave-one-out cross validation, the proposed model achieved
a median Dice of 0.75, which was significantly better (p<0.01) than the two
competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th
International Symposium on Biomedical Imaging (ISBI 2018
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
- …