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

Liu, Jia; Chen, Fang; Pan, Changcun; Zhu, Mingyu; Zhang, Xinran; Zhang, Liwei; Liao, Hongen

doi:10.1109/tbme.2018.2845706

Cited by 74 publications

(44 citation statements)

References 30 publications

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“…Girish et al [13] used deep convolutional neural networks for segmentation of retinal vessels and performed well in retinal vessel segmentation. Liu et al [14] researchers applied three-dimensional convolutional neural networks to the study of brain lesion segmentation, which has made great progress in clinical applications. Farabet et al [15] first used a multi-scale convolutional neural network to extract dense features from original medical exercise rehabilitation images.…”

Section: Related Workmentioning

confidence: 99%

“…Because the network extracts the local area features, it can express the objects in the area well, so finally the better regional classification results can be obtained. Some region-based methods [14], [20] use over-segmentation to produce regions of any shape that are small, non-overlapping, and then use CNN to extract region features. Because it is an area of arbitrary shape, this feature can better express irregular areas than rectangular areas.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, the most commonly used Gaussian smoothing filter is used to preprocess the image. The calculation formula of the filtering is shown in equation (14). (14) where the variable σ is the standard deviation of the gray value and the variable n is the dimension of the Gaussian convolution kernel.…”

Section: ) Filter Smoothingmentioning

confidence: 99%

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Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Ding

Ren

2019

IEEE Access

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Deep learning has achieved great success in the field of computer vision, and the precision in image classification and image detection has surpassed humans. Therefore, this paper combines deep learning and medical image segmentation, focusing on how to improve the accuracy and speed of segmentation algorithm of medical exercise rehabilitation image. Aiming at the shortcomings of traditional medical image recognition methods, a medical exercise rehabilitation image segmentation algorithm based on hierarchical features of convolutional neural networks is proposed, this paper calls it as hierarchical features of convolutional neural networks (HFCNN). The algorithm firstly samples the convolution output of multiple layers in the convolutional neural network to a unified scale and combines them to construct a hierarchical feature. This hierarchical feature combines the structural information of objects contained in the shallow layer of the network with the semantic information of objects contained in the deep layers of the network, so it has a strong ability to express. Secondly, the image can be segmented into multiple super pixels by the super pixel segmentation algorithm. The classifier is trained using the hierarchical features of the super pixel, and then the classification result is mapped back to the pixel. Finally, a fully connected conditional random field algorithm including one-potential potential energy and paired potential energy is constructed. The corresponding energy function is used to smooth the classification result of the pixel, and the regional consistency and continuity of the pixel mark are improved. Compared with many classical convolutional neural network algorithms, this algorithm not only accelerates the network convergence speed, shortens the training time, but also significantly improves the accuracy of segmentation algorithm of medical exercise rehabilitation image, showing good practical value.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Ding

Ren

2019

IEEE Access

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“…For example , Liu et. al. combined CNN features and a SVM classifier to automatically predict genotypes from brain MRIs [3]. An end-to-end learning allows features learning and classifier training to work collaboratively, resulting in a more meaningful and stronger deep representation that leads to state-of-the-art performance on brain tumor segmentation [1,2,[4][5][6][7] and stroke lesion segmentation [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Brain SegNet: 3D local refinement network for brain lesion segmentation

Luo

et al. 2020

BMC Med Imaging

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“…al. combined CNN features and a SVM classifier to automatically predict genotypes from brain MRIs [3]. An end-to-end learning allows features learning and classifier training to work collaboratively, resulting in a more meaningful and stronger deep representation that leads to state-of-the-art performance on brain tumor segmentation [4,5,2,6,1,7] and stroke lesion segmentation [8,9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

Luo

et al. 2020

Preprint

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MR images (MRIs) accurate segmentation of brain lesions is important for improving cancer diagnosis, surgical planning, and prediction of outcome. However, manual and accurate segmentation of brain lesions from 3D MRIs is highly expensive, time-consuming, and prone to user biases. We present an efficient yet conceptually simple brain segmentation network (referred as Brain SegNet), which is a 3D residual framework for automatic voxel-wise segmentation of brain lesion. Our model is able to directly predict dense voxel segmentation of brain tumor or ischemic stroke regions in 3D brain MRIs. The proposed 3D segmentation network can run at about 0.5s per MRIs - about 50 times faster than previous approaches [1,2]. Our model is evaluated on the BRATS 2015 benchmark for brain tumor segmentation, where it obtains state-of-the-art results, by surpassing recently published results reported in [1,2]. We further applied the proposed Brain SegNet for ischemic stroke lesion outcome prediction, with impressive results achieved on the Ischemic Stroke Lesion Segmentation (ISLES) 2017 database.

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

Abstract: 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.

Cited by 74 publications

References 30 publications

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Segmentation Algorithm of Medical Exercise Rehabilitation Image Based on HFCNN and IoT

Brain SegNet: 3D local refinement network for brain lesion segmentation

Brain SegNet: 3D Local Refinement Network for Brain Lesion Segmentation

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