The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
Open accessAbstract
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.
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Citations
Cites
ImageNet classification with deep convolutional neural networks
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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
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Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
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Brain tumor segmentation with Deep Neural Networks
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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
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“…from the unique feature learning ability from hierarchical network layers, CNN models have shown encouraging results in medical image segmentation tasks (Moeskops et al., 2016; Valverde et al., 2017; Zhang et al., 2015), indicating the usefulness of CNN-based models for medical object segmentation.”
BackgroundDeep convolutional neural networks for multi-modality isointense infant brain image segmentation
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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network
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Cited by
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., Shen, J., & Shao, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2996645 · Cited by 1,259
Deep learning with multimodal representation for pancancer prognosis prediction
Cheerla, A., & Gevaert, O. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz342 · Cited by 421
Self-citationSupervised uncertainty quantification for segmentation with multiple annotations
Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., & Welling, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32245-8_16 · Cited by 105
Zhang, L., Dong, D., Sun, Y., Hu, C., Sun, C., Wu, Q., & Tian, J. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.17854 · Cited by 42
Kim, Y., Park, J. Y., Hwang, E. J., Lee, S. M., & Park, C. M. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-1342 · Cited by 31
Metadata
- Authors
- Wang, S., Zhou, M., Liu, Z., Liu, Z., Gu, D., Zang, Y., Dong, D., Gevaert, O., & Tian, J.
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 40
- Pages
- 172-183
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2732063980
- Cited by
- 478
- References
- 13
- FWCI
- 17.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
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