2 research outputs found
A CADe System for Gliomas in Brain MRI using Convolutional Neural Networks
Inspired by the success of Convolutional Neural Networks (CNN), we develop a
novel Computer Aided Detection (CADe) system using CNN for Glioblastoma
Multiforme (GBM) detection and segmentation from multi channel MRI data. A
two-stage approach first identifies the presence of GBM. This is followed by a
GBM localization in each "abnormal" MR slice. As part of the CADe system, two
CNN architectures viz. Classification CNN (C-CNN) and Detection CNN (D-CNN) are
employed. The CADe system considers MRI data consisting of four sequences
(, , and ) as input, and automatically
generates the bounding boxes encompassing the tumor regions in each slice which
is deemed abnormal. Experimental results demonstrate that the proposed CADe
system, when used as a preliminary step before segmentation, can allow improved
delineation of tumor region while reducing false positives arising in normal
areas of the brain. The GrowCut method, employed for tumor segmentation,
typically requires a foreground and background seed region for initialization.
Here the algorithm is initialized with seeds automatically generated from the
output of the proposed CADe system, thereby resulting in improved performance
as compared to that using random seeds.Comment: The paper consists of 11 Pages, 6 Figures, 7 Tables, 56 Reference
Deep Radiomics for Brain Tumor Detection and Classification from Multi-Sequence MRI
Glioma constitutes 80% of malignant primary brain tumors and is usually
classified as HGG and LGG. The LGG tumors are less aggressive, with slower
growth rate as compared to HGG, and are responsive to therapy. Tumor biopsy
being challenging for brain tumor patients, noninvasive imaging techniques like
Magnetic Resonance Imaging (MRI) have been extensively employed in diagnosing
brain tumors. Therefore automated systems for the detection and prediction of
the grade of tumors based on MRI data becomes necessary for assisting doctors
in the framework of augmented intelligence. In this paper, we thoroughly
investigate the power of Deep ConvNets for classification of brain tumors using
multi-sequence MR images. We propose novel ConvNet models, which are trained
from scratch, on MRI patches, slices, and multi-planar volumetric slices. The
suitability of transfer learning for the task is next studied by applying two
existing ConvNets models (VGGNet and ResNet) trained on ImageNet dataset,
through fine-tuning of the last few layers. LOPO testing, and testing on the
holdout dataset are used to evaluate the performance of the ConvNets. Results
demonstrate that the proposed ConvNets achieve better accuracy in all cases
where the model is trained on the multi-planar volumetric dataset. Unlike
conventional models, it obtains a testing accuracy of 95% for the low/high
grade glioma classification problem. A score of 97% is generated for
classification of LGG with/without 1p/19q codeletion, without any additional
effort towards extraction and selection of features. We study the properties of
self-learned kernels/ filters in different layers, through visualization of the
intermediate layer outputs. We also compare the results with that of
state-of-the-art methods, demonstrating a maximum improvement of 7% on the
grading performance of ConvNets and 9% on the prediction of 1p/19q codeletion
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