+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas



A Cascaded Deep Convolutional Neural Network for Joint Segmentation and Genotype Prediction of Brainstem Gliomas



IEEE Transactions on Bio-Medical Engineering 65(9): 1943-1952



Automatic segmentation of brainstem gliomas and prediction of genotype (H3 K27M) mutation status based on magnetic resonance (MR) images are crucial but challenging tasks for computer-aided diagnosis in neurosurgery. In this paper, we present a novel cascaded deep convolutional neural network (CNN) to address these two challenging tasks simultaneously. Our novel segmentation task contains two feature-fusion modules: the Gaussian-pyramid multiscale input features-fusion technique and the brainstem-region feature enhancement. The aim is to resolve very difficult problems in brainstem glioma segmentation. Our prediction model combines CNN features and support-vector-machine classifier to automatically predict genotypes without region-of-interest labeled-MR images and is learned jointly with the segmentation task. First, Gaussian-pyramid multiscale input feature fusion is added to our glioma-segmentation task to solve the problems of size variety and weak brainstem-gliomas boundaries. Second, the two feature-fusion modules provide both local and global contexts to retain higher frequency details for sharper tumor boundaries, handling the problem of the large variation of tumor shape, and volume resolution. Experiments demonstrate that our cascaded CNN method achieves not only a good tumor segmentation result with a high Dice similarity coefficient of 77.03%, but also a competitive genotype prediction result with an average accuracy of 94.85% upon fivefold cross-validation.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 043273138

Download citation: RISBibTeXText

PMID: 29993462

DOI: 10.1109/tbme.2018.2845706


Related references

Brain tumor segmentation using cascaded deep convolutional neural network. Conference Proceedings 2017: 1998-2001, 2017

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network. Journal of Healthcare Engineering 2018: 4940593, 2018

Deep convolutional neural network for segmentation of knee joint anatomy. Magnetic Resonance in Medicine 80(6): 2759-2770, 2018

Joint Reconstruction and Segmentation of 7T-like MR Images from 3T MRI Based on Cascaded Convolutional Neural Networks. Medical Image Computing and Computer-Assisted Intervention 10433: 764-772, 2017

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach. Neuroimage 155: 159-168, 2017

Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study. Plos one 13(4): E0195798, 2018

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network. Computer Methods and Programs in Biomedicine 159: 59-69, 2018

Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Medical Image Computing and Computer-Assisted Intervention 16(Pt 2): 246-253, 2013

Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images. Medical Physics 46(5): 2169-2180, 2019

Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography. Medical Physics 46(2): 634-648, 2019

Deep Learning-Based Segmentation and Quantification of Cucumber Powdery Mildew Using Convolutional Neural Network. Frontiers in Plant Science 10: 155, 2019

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Medical Physics 43(4): 1882, 2016

Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning. Neuroinformatics 17(4): 563-582, 2019

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Computers in Biology and Medicine 95: 43-54, 2018

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors 17(4):, 2017