Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster

A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation

We propose a Transformer architecture for volumetric segmentation, a challenging task that requires keeping a complex balance in encoding local and global spatial cues, and preserving information along all axes of the volume. Encoder of the proposed design benefits from self-attention mechanism to simultaneously encode local and global cues, while the decoder employs a parallel self and cross attention formulation to capture fine details for boundary refinement. Empirically, we show that the proposed design choices result in a computationally efficient model, with competitive and promising results on the Medical Segmentation Decathlon (MSD) brain tumor segmentation (BraTS) Task. We further show that the representations learned by our model are robust against data corruptions. \href{https://github.com/himashi92/VT-UNet}{Our code implementation is publicly available}

Brain Tumor Detection Based on a Novel and High-Quality Prediction of the Tumor Pixel Distributions

In this paper, we propose a system to detect brain tumor in 3D MRI brain scans of Flair modality. It performs 2 functions: (a) predicting gray-level and locational distributions of the pixels in the tumor regions and (b) generating tumor mask in pixel-wise precision. To facilitate 3D data analysis and processing, we introduced a 2D histogram presentation that comprehends the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, particular 2D histograms, in which tumor-related feature data get concentrated, are established by exploiting the left-right asymmetry of a brain structure. A modulation function is generated from the input data of each patient case and applied to the 2D histograms to attenuate the element irrelevant to the tumor regions. The prediction of the tumor pixel distribution is done in 3 steps, on the axial, coronal and sagittal slice series, respectively. In each step, the prediction result helps to identify/remove tumor-free slices, increasing the tumor information density in the remaining data to be applied to the next step. After the 3-step removal, the 3D input is reduced to a minimum bounding box of the tumor region. It is used to finalize the prediction and then transformed into a 3D tumor mask, by means of gray level thresholding and low-pass-based morphological operations. The final prediction result is used to determine the critical threshold. The proposed system has been tested extensively with the data of more than one thousand patient cases in the datasets of BraTS 2018~21. The test results demonstrate that the predicted 2D histograms have a high degree of similarity with the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs, which is achieved at an extremely low computation cost and no need for training

Segmentation of Brain Tumor Using a 3D Generative Adversarial Network

Data Availability Statement: Used dataset is available in: https://www.med.upenn.edu/cbica/brats2021/ and prepared model is available in: https://github.com/hamyadkiani/3D-GAN accessed on 7 September 2023.Copyright © 2023 by the authors. Images of brain tumors may only show up in a small subset of scans, so important details may be missed. Further, because labeling is typically a labor-intensive and time-consuming task, there are typically only a small number of medical imaging datasets available for analysis. The focus of this research is on the MRI images of the human brain, and an attempt has been made to propose a method for the accurate segmentation of these images to identify the correct location of tumors. In this study, GAN is utilized as a classification network to detect and segment of 3D MRI images. The 3D GAN network model provides dense connectivity, followed by rapid network convergence and improved information extraction. Mutual training in a generative adversarial network can bring the segmentation results closer to the labeled data to improve image segmentation. The BraTS 2021 dataset of 3D images was used to compare two experimental models.This research received no external funding