Learning Contextual and Attentive Information for Brain Tumor Segmentation

Zhou, Chenhong; Chen, Shengcong; Ding, Changxing; Tao, Dacheng

doi:10.1007/978-3-030-11726-9_44

Cited by 83 publications

(57 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors also introduce a new loss function, a generalization of binary crossentropy, to account for label uncertainty. Finally, Zhou et al [23] proposed to use an ensemble of different networks: taking into account multi-scale context information, segmenting 3 tumor subregions in cascade with a shared backbone weights and adding an attention block.…”

Section: Related Workmentioning

confidence: 99%

3D MRI Brain Tumor Segmentation Using Autoencoder Regularization

Myronenko

2019

Lecture Notes in Computer Science

834

517

View full text Add to dashboard Cite

Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.

show abstract

Section: Related Workmentioning

confidence: 99%

3D MRI Brain Tumor Segmentation Using Autoencoder Regularization

Myronenko

2019

Lecture Notes in Computer Science

834

517

View full text Add to dashboard Cite

show abstract

“…(2) The methods based on deep learning [2], [4], [7], [9]. Recently, due to the strong robustness and versatility of convolution neural networks (CNNs), research on medical image processing using deep learning algorithms has become a contemporary hot spot, which outperforms the traditional methods significantly [8], [10]. Miki et al [40] used AlexNet [44] network architecture and image data enhancement strategy to realize the classification of teeth, which can be used for automatic filing of dental records for forensic identification.…”

Section: Introductionmentioning

confidence: 99%

A Symmetric Fully Convolutional Residual Network With DCRF for Accurate Tooth Segmentation

et al. 2020

View full text Add to dashboard Cite

Accurate tooth segmentation from CBCT images is a crucial step for specialist to perform quantitative analysis, clinical diagnosis and operation. In this paper, we present a symmetric full convolutional network with residual block and Dense Conditional Random Field (DCRF), which can achieve accurate segmentation automatically for tooth images. The proposed method can not only strengthen feature propagation, but also boost feature reuse, which can be credited to the contracting path and the expanding path that extract and recover pixel cues sufficiently. To this end, we apply special deep bottleneck architectures (DBAs) and summation-based skip connection into our network to ensure accurate segmentation for much deeper neural network. Compared with previous methods which are based on conditional random field for original image intensity, our approach applies DCRF to the posterior probability generated by the proposed network. To avoid the interferences of noises around the tooth, we combine the pixel-level prediction capability of DCRF, which further enhance the segmentation performance. In the experiments, we verify the capabilities of our methods based on four evaluation indicators, which demonstrates the superiority of our method. INDEX TERMS Tooth segmentation, CBCT images, fully convolutional network, bottleneck architecture, conditional random field.

show abstract

“…McKinley et al (2018) also proposed a U-Net-like network and introduce a new loss function, a generalization of binary cross-entropy, to account for label uncertainty. Furthermore, Zhou et al (2018) explored the ensemble of different networks including multi-scale context information, and also segmented three tumor compartments in cascade with an additional attention block.…”

Section: Introductionmentioning

confidence: 99%