CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

Liu, Hongying; Shen, Xiongjie; Shang, Fanhua; Ge, Feihang; Wang, Fei

doi:10.1007/978-3-030-33226-6_12

Cited by 44 publications

(21 citation statements)

References 11 publications

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“…Cascading U‐nets has shown great ability in dense prediction tasks, such as medical image segmentation 27,28 . And in this study, it is firstly applied to dose prediction.…”

Section: Methodsmentioning

confidence: 99%

Technical Note: A cascade 3D U‐Net for dose prediction in radiotherapy

Liu

Zhang

et al. 2021

Medical Physics

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Purpose Although large datasets are available, to learn a robust dose prediction model from a limited dataset still remains challenging. This work employed cascaded deep learning models and advanced training strategies with a limited dataset to precisely predict three‐dimensional (3D) dose distribution. Methods A Cascade 3D (C3D) model is developed based on the cascade mechanism and 3D U‐Net network units. During model training, data augmentations are used to improve the generalization ability of the prediction model. A knowledge distillation technique is employed to further improve the capability of model learning. The C3D network was evaluated using the OpenKBP challenge dataset and competed with those models proposed by more than 40 teams globally. Additionally, it was compared with five existing cutting‐edge dose prediction models. The performance of these prediction models was evaluated by voxel‐based mean absolute error (MAE) and clinical‐related dosimetric metrics. The code and models are publicly available online (https://github.com/LSL000UD/RTDosePrediction). Results The MAE of a single C3D model without test‐time augmentation is 2.50 Gy (3.57% related to prescription dose) for nonzero dose area, which outperforms the other five dose prediction models by about 0.1 Gy–1.7 Gy. The C3D model won both dose and DVH streams of AAPM 2020 OpenKBP challenge with dose score of 2.31 and DVH score of 1.55. Conclusions The Cascading U‐Nets is an ideal solution for 3D dose prediction from a limited dataset. The proper data preprocessing, data augmentation, and optimization procedure are more important than architectural modifications of deep learning network.

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“…Cascading U‐nets has shown great ability in dense prediction tasks, such as medical image segmentation 27,28 . And in this study, it is firstly applied to dose prediction.…”

Section: Methodsmentioning

confidence: 99%

Technical Note: A cascade 3D U‐Net for dose prediction in radiotherapy

Liu

Zhang

et al. 2021

Medical Physics

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“…The qualitative segmentation results of different methods are presented in Figure 7, and we can see that fine details could be better recovered by our method. Even though the performance reported here did not represent the state-of-the-art performance on BRATS 2017 [40], it demonstrated that replacing the 3D convolution kernels by the proposed 3D-DSC was able to reduce the risk of overfitting and hence improve the performance via a deeper network.…”

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confidence: 65%

3D Dense Separated Convolution Module for Volumetric Medical Image Analysis

Zou

2020

Applied Sciences

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With the thriving of deep learning, 3D Convolutional Neural Networks have become a popular choice in volumetric image analysis due to their impressive 3D contexts mining ability. However, the 3D convolutional kernels will introduce a significant increase in the amount of trainable parameters. Considering the training data is often limited in biomedical tasks, a tradeoff has to be made between model size and its representational power. To address this concern, in this paper, we propose a novel 3D Dense Separated Convolution (3D-DSC) module to replace the original 3D convolutional kernels. The 3D-DSC module is constructed by a series of densely connected 1D filters. The decomposition of 3D kernel into 1D filters reduces the risk of over-fitting by removing the redundancy of 3D kernels in a topologically constrained manner, while providing the infrastructure for deepening the network. By further introducing nonlinear layers and dense connections between 1D filters, the network's representational power can be significantly improved while maintaining a compact architecture. We demonstrate the superiority of 3D-DSC on volumetric image classification and segmentation, which are two challenging tasks often encountered in biomedical image computing.

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“…The UNet-LSTM-UNet appends another UNet as an additional image pre-processing step before each slice is fed into the LSTM-UNet. This design, inspired by cascading architectures for brain tumor segmentation [36], similarly allows for variable slice number volumes, 3D-2D segmentation, and provides increased numbers of trainable convolutions and parameters earlier within the architecture, potentially allowing for improved labeling of vessels within each slice.…”

Section: Nested Model Architecturesmentioning

confidence: 99%

Novel Application of Long Short-Term Memory Network for 3D to 2D Retinal Vessel Segmentation in Adaptive Optics—Optical Coherence Tomography Volumes

Wang

Villanueva

et al. 2021

Applied Sciences

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Adaptive optics—optical coherence tomography (AO-OCT) is a non-invasive technique for imaging retinal vascular and structural features at cellular-level resolution. Whereas retinal blood vessel density is an important biomarker for ocular diseases, particularly glaucoma, automated blood vessel segmentation tools in AO-OCT have not yet been explored. One reason for this is that AO-OCT allows for variable input axial dimensions, which are not well accommodated by 2D-2D or 3D-3D segmentation tools. We propose a novel bidirectional long short-term memory (LSTM)-based network for 3D-2D segmentation of blood vessels within AO-OCT volumes. This technique incorporates inter-slice connectivity and allows for variable input slice numbers. We compare this proposed model to a standard 2D UNet segmentation network considering only volume projections. Furthermore, we expanded the proposed LSTM-based network with an additional UNet to evaluate how it refines network performance. We trained, validated, and tested these architectures in 177 AO-OCT volumes collected from 18 control and glaucoma subjects. The LSTM-UNet has statistically significant improvement (p < 0.05) in AUC (0.88) and recall (0.80) compared to UNet alone (0.83 and 0.70, respectively). LSTM-based approaches had longer evaluation times than the UNet alone. This study shows that a bidirectional convolutional LSTM module improves standard automated vessel segmentation in AO-OCT volumes, although with higher time cost.

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CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

Cited by 44 publications

References 11 publications

Technical Note: A cascade 3D U‐Net for dose prediction in radiotherapy

Technical Note: A cascade 3D U‐Net for dose prediction in radiotherapy

3D Dense Separated Convolution Module for Volumetric Medical Image Analysis

Novel Application of Long Short-Term Memory Network for 3D to 2D Retinal Vessel Segmentation in Adaptive Optics—Optical Coherence Tomography Volumes

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