Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis

2017-12-06 · Varghese Alex, Mohammed Safwan, Ganapathy Krishnamurthi

In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI). A fully automatic, voxel based classification was achieved by training a 23 layer deep FCNN on 2-D slices extracted from patient volumes. The network was trained on slices extracted from 130 patients and validated on 50 patients. For the task of survival prediction, texture and shape based features were extracted from T1 post contrast volume to train an XGBoost regressor. On BraTS 2017 validation set, the proposed scheme achieved a mean whole tumor, tumor core and active dice score of 0.83, 0.69 and 0.69 respectively and an accuracy of 52% for the overall survival prediction.

📄 PDF Abstract BibTeX arXiv:1712.02066

Code (0)

등록된 구현이 없습니다.

Tasks

General Classification PredictionSurvival PredictionTexture Classification

Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction

2019-11-19 · Shuo Wang, Chengliang Dai, Yuanhan Mo, Elsa Angelini 외

Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance,…

Image Segmentation Medical Image Segmentation Prediction Segmentation+2

Domain Knowledge Based Brain Tumor Segmentation and Overall Survival Prediction

2019-12-16 · Xiaoqing Guo, Chen Yang, Pak Lun Lam, Peter Y. M. Woo 외

Automatically segmenting sub-regions of gliomas (necrosis, edema and enhancing tumor) and accurately predicting overall survival (OS) time from multimodal MRI sequences have important clinical significance in diagnosis, …

Brain Tumor Segmentation Position Prognosis Segmentation+2

3D Semantic Segmentation of Brain Tumor for Overall Survival Prediction

2020-08-25 · Rupal Agravat, Mehul S. Raval

Glioma, the malignant brain tumor, requires immediate treatment to improve the survival of patients. Gliomas heterogeneous nature makes the segmentation difficult, especially for sub-regions like necrosis, enhancing tumo…

3D Semantic Segmentation Decoder Segmentation Semantic Segmentation+1

Multi-Resolution 3D CNN for MRI Brain Tumor Segmentation and Survival Prediction

2019-11-19 · Mehdi Amian, Mohammadreza Soltaninejad

In this study, an automated three dimensional (3D) deep segmentation approach for detecting gliomas in 3D pre-operative MRI scans is proposed. Then, a classi-fication algorithm based on random forests, for survival predi…

Brain Tumor Segmentation Segmentation Survival Prediction Tumor Segmentation

Brain Tumor Segmentation and Survival Prediction using Automatic Hard mining in 3D CNN Architecture

2021-01-05 · Vikas Kumar Anand, Sanjeev Grampurohit, Pranav Aurangabadkar, Avinash Kori 외

We utilize 3-D fully convolutional neural networks (CNN) to segment gliomas and its constituents from multimodal Magnetic Resonance Images (MRI). The architecture uses dense connectivity patterns to reduce the number of …

Brain Tumor Segmentation Survival Prediction Tumor Segmentation