Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI

2019 ◽  
pp. 574-582 ◽  
Author(s):  
Tongxue Zhou ◽  
Su Ruan ◽  
Haigen Hu ◽  
Stéphane Canu
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Dilated Convolution ◽  
Deep Learning Model

A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

2019 ◽  
Vol 39 (3) ◽  
pp. 613-623 ◽  
Author(s):  
Tiejun Yang ◽  
Jikun Song ◽  
Lei Li
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Random Forests ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Deep Learning Model

scholarly journals A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

2018 ◽  
Vol 43 ◽  
pp. 98-111 ◽  
Author(s):  
Xiaomei Zhao ◽  
Yihong Wu ◽  
Guidong Song ◽  
Zhenye Li ◽  
Yazhuo Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Deep Learning Model

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

2019 ◽  
Vol 82 (8) ◽  
pp. 1302-1315 ◽  
Author(s):  
Sajid Iqbal ◽  
Muhammad U. Ghani Khan ◽  
Tanzila Saba ◽  
Zahid Mehmood ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Deep Learning Model

RBIO-03. INITIAL RESULT OF DEVELOP ROBUST DEEP LEARNING MODEL FOR DETECTING GENOMIC STATUS IN GLIOMAS AGAINST IMAGE DIFFERENCES AMONG FACILITIES

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi192-vi192
Author(s):  
Satoshi Takahashi ◽  
Masamichi Takahashi ◽  
Manabu Kinoshita ◽  
Mototaka Miyake ◽  
Jun Sese ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Idh Mutation ◽  
Tert Promoter Mutation ◽  
Promoter Mutation ◽  
Test Portion ◽  
Tert Promoter ◽  
Deep Learning Model

Abstract BACKGROUND The importance of detecting the genomic status of gliomas is increasingly recognized and IDH (isocitrate dehydrogenase) mutation and TERT (telomerase reverse transcriptase) promoter mutation have a significant impact on treatment decisions. Noninvasive prediction of these genomic statuses in gliomas is a challenging problem; however, a deep learning model using magnetic resonance imaging (MRI) can be a solution. The image differences among facilities causing performance degradation, called domain shift, have also been reported in other tasks such as brain tumor segmentation. We investigated whether a deep learning model could predict the gene status, and if so, to what extent it would be affected by domain shift. METHOD We used the Multimodal Brain Tumor Segmentation Challenge (BraTS) data and the Japanese cohort (JC) dataset consisted of brain tumor images collected from 544 patients in 10 facilities in Japan. We focused on IDH mutation and TERT promoter mutation. The deep learning models to predict the statuses of these genes were trained by the BraTS dataset or the training portion of the JC dataset, and the test portion of the JC dataset evaluated the accuracy of the models. RESULTS The IDH mutation predicting model trained by the BraTS dataset showed 80.0% accuracy for the validation portion of the BraTS dataset; however, only 67.3% for the test portion of the JC dataset. The TERT promoter mutation predicting model trained by the training portion of the JC dataset showed only 49% accuracy for the test portion of the JC dataset. CONCLUSION IDH mutation can be predicted by deep learning models using MRI, but the performance degeneration by domain shift was significant. On the other hand, TERT promoter mutation could not be predicted accurately enough by current deep learning techniques. In both mutations, further studies are needed.

scholarly journals IMG-22. A DEEP LEARNING MODEL FOR AUTOMATIC POSTERIOR FOSSA PEDIATRIC BRAIN TUMOR SEGMENTATION: A MULTI-INSTITUTIONAL STUDY

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii359-iii359
Author(s):  
Lydia Tam ◽  
Edward Lee ◽  
Michelle Han ◽  
Jason Wright ◽  
Leo Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Posterior Fossa ◽  
Ground Truth ◽  
Learning Model ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Study Cohort ◽  
Navigation Systems ◽  
Tumor Types ◽  
Deep Learning Model

Abstract BACKGROUND Brain tumors are the most common solid malignancies in childhood, many of which develop in the posterior fossa (PF). Manual tumor measurements are frequently required to optimize registration into surgical navigation systems or for surveillance of nonresectable tumors after therapy. With recent advances in artificial intelligence (AI), automated MRI-based tumor segmentation is now feasible without requiring manual measurements. Our goal was to create a deep learning model for automated PF tumor segmentation that can register into navigation systems and provide volume output. METHODS 720 pre-surgical MRI scans from five pediatric centers were divided into training, validation, and testing datasets. The study cohort comprised of four PF tumor types: medulloblastoma, diffuse midline glioma, ependymoma, and brainstem or cerebellar pilocytic astrocytoma. Manual segmentation of the tumors by an attending neuroradiologist served as “ground truth” labels for model training and evaluation. We used 2D Unet, an encoder-decoder convolutional neural network architecture, with a pre-trained ResNet50 encoder. We assessed ventricle segmentation accuracy on a held-out test set using Dice similarity coefficient (0–1) and compared ventricular volume calculation between manual and model-derived segmentations using linear regression. RESULTS Compared to the ground truth expert human segmentation, overall Dice score for model performance accuracy was 0.83 for automatic delineation of the 4 tumor types. CONCLUSIONS In this multi-institutional study, we present a deep learning algorithm that automatically delineates PF tumors and outputs volumetric information. Our results demonstrate applied AI that is clinically applicable, potentially augmenting radiologists, neuro-oncologists, and neurosurgeons for tumor evaluation, surveillance, and surgical planning.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

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