One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation

Zhou, Chenhong; Ding, Changxing; Lu, Zhentai; Wang, Xinchao; Tao, Dacheng

doi:10.1007/978-3-030-00931-1_73

Cited by 64 publications

(33 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used dilated convolutions after the down-sampling layers and set the dilation parameter from 1 to 3. Zhou et al (2018) presented a one-single multitask CNN that can learn the correlations between different categories. These two methods used a cascade or cascade-like training strategy like our training process, and they both obtained high Dice scores in the brain tumor segmentation task.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…They then sequentially enhanced tumor core. Zhou et al (2018) drew upon lesions with coarse-to-fine medical image segmentation methods and proposed a single multitask CNN that could learn correlations between different categories. Partial model parameters can be shared when different tasks are being trained according to different sets of training data to utilize the underlying correlation among classes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Wang

Chen

et al. 2019

Front. Neurosci.

View full text Add to dashboard Cite

Aim: Brain tumors are among the most fatal cancers worldwide. Diagnosing and manually segmenting tumors are time-consuming clinical tasks, and success strongly depends on the doctor's experience. Automatic quantitative analysis and accurate segmentation of brain tumors are greatly needed for cancer diagnosis. Methods: This paper presents an advanced three-dimensional multimodal segmentation algorithm called nested dilation networks (NDNs). It is inspired by the U-Net architecture, a convolutional neural network (CNN) developed for biomedical image segmentation and is modified to achieve better performance for brain tumor segmentation. Thus, we propose residual blocks nested with dilations (RnD) in the encoding part to enrich the low-level features and use squeeze-and-excitation (SE) blocks in both the encoding and decoding parts to boost significant features. To prove the reliability of the network structure, we compare our results with those of the standard U-Net and its transmutation networks. Different loss functions are considered to cope with class imbalance problems to maximize the brain tumor segmentation results. A cascade training strategy is employed to run NDNs for coarse-to-fine tumor segmentation. This strategy decomposes the multiclass segmentation problem into three binary segmentation problems and trains each task sequentially. Various augmentation techniques are utilized to increase the diversity of the data to avoid overfitting. Results: This approach achieves Dice similarity scores of 0.6652, 0.5880, and 0.6682 for edema, non-enhancing tumors, and enhancing tumors, respectively, in which the Dice loss is used for single-pass training. After cascade training, the Dice similarity scores rise to 0.7043, 0.5889, and 0.7206, respectively. Conclusion: Experiments show that the proposed deep learning algorithm outperforms other U-Net transmutation networks for brain tumor segmentation. Moreover, applying cascade training to NDNs facilitates better performance than other methods. The findings of this study provide considerable insight into the automatic and accurate segmentation of brain tumors.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Wang

Chen

et al. 2019

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Working with a similar architecture, Pereira et al [41] set forth an FCN which captured more sophisticated features through feature recombination and also introduced a recalibration block in the structure. Zhou et al [42] proposed a multi-task CNN, which integrated and trained on the different tasks of brain tumour segmentation in terms of their correlation and simplified the inference process through a one-pass computational scheme. Ji et al [43] proposed a weakly-supervised U-Net that employed a scribble-based approach.…”

Section: B Deep Learning Architecturesmentioning

confidence: 99%

Brain Tumour Image Segmentation Using Deep Networks

et al. 2020

View full text Add to dashboard Cite

Automated segmentation of brain tumour from multimodal MR images is pivotal for the analysis and monitoring of disease progression. As gliomas are malignant and heterogeneous, efficient and accurate segmentation techniques are used for the successful delineation of tumours into intra-tumoural classes. Deep learning algorithms outperform on tasks of semantic segmentation as opposed to the more conventional, context-based computer vision approaches. Extensively used for biomedical image segmentation, Convolutional Neural Networks have significantly improved the state-of-the-art accuracy on the task of brain tumour segmentation. In this paper, we propose an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions. Both models were trained separately on the BraTS-19 challenge dataset and evaluated to yield segmentation maps which considerably differed from each other in terms of segmented tumour sub-regions and were ensembled variably to achieve the final prediction. The suggested ensemble achieved dice scores of 0.750, 0.906 and 0.846 for enhancing tumour, whole tumour, and tumour core, respectively, on the validation set, performing favourably in comparison to the state-of-the-art architectures currently available.

show abstract

“…WNet, TNet, and ENet are designed to segment substructures of the brain tumor, and each of these networks settles a binary segmentation problem. One-Pass Multi-task Network (OM-Net) [40] exploits the correlation between classes in training and simplifies the cascade inference processed by one-pass computation. A comparison between NDN and these two current published algorithms is listed in TABLE 1 and the corresponding example images are shown in Fig.…”

Section: B Brain Tumormentioning

confidence: 99%

Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

et al. 2019

View full text Add to dashboard Cite

The deep convolutional network has shown excellent performance in medical image analysis. However, almost all network variants are presented for one specific task, e.g., segment pancreas on computerized tomography (CT). In this paper, we propose a nested dilation network (NDN) which is applied to multiple segmentation tasks even for different modalities, including CT, magnetic resonance imaging (MRI), and endoscopic images. We design residual blocks nested with dilations (RnD Blocks) that catch larger receptive field in the first few layers to boost shallow semantic information. Besides, we apply the modified focal loss to help the network to provide more accurate segmentation results. We evaluate our method on five subtasks from medical segmentation decathlon challenge and GIANA2018, and the results show that our method achieves a better performance than the latest methods in each task. A lot of research works have been done recently to strengthen the learning power of the convolutional neural network (CNN) to get better performance.INDEX TERMS Deep learning, medical segmentation, residual blocks nested with dilations, multi-task, focal loss.

show abstract

One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation

Cited by 64 publications

References 12 publications

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Brain Tumour Image Segmentation Using Deep Networks

Nested Dilation Network (NDN) for Multi-Task Medical Image Segmentation

Contact Info

Product

Resources

About