Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation

By Shuo Wang, Mu Zhou, Zaiyi Liu, Zhenyu Liu, D. Gu, Yali Zang, Di Dong, Olivier Gevaert, Jie Tian. Medical Image Anal., 2017

470 citations recorded by the source corpus.

HighlightsA data‐driven lung nodule segmentation method without involving shape hypothesis.Two‐branch convolutional neural networks extract both 3D and multi‐scale 2D features.A novel central pooling layer is proposed for feature selection.We propose a weighted sampling method to solve imbalanced training label problem.The method shows strong performance for segmenting juxtapleural nodules. Graphical abstract Figure. No caption available. &NA; 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%.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/76d62b8ed79bca36a85d49d5aef2fe8fb1c14442

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pulmonary and Respiratory Medicine

1. Ning Guo
2. Quanzheng Li
3. Xiang Li
4. Zhe Guo
5. Heng Huang

In Radiology, Nuclear Medicine and Imaging

1. Huan Fu
2. Anika Cheerla
3. Jiang Liu
4. Jun Cheng
5. Ling Shao
6. Jianbing Shen
7. Tao Zhou
8. Deng-Ping Fan

Citations in the corpus

• UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation (2021, unknown): “Currently, the convolutional encoderdecoder architectures have made substantial progress in position-sensitive tasks, like semantic segmentation [14,11,20,17,6].”
• Deep learning with multimodal representation for pancancer prognosis prediction (2019, background): “A key component of the success of CNNs is their 93 ability to deal with high-dimensional, unstructured data, in particular image data [30].”
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, background): “[29] developed a central focused convolutional neural network to segment lung nodules from heterogeneous CT scans.”
• CE-Net: Context Encoder Network for 2D Medical Image Segmentation (2019, methodology): “MEDICAL image segmentation is often an important step in medical image analysis, such as optic disc segmentation [1]–[3] and blood vessel detection [4]–[8] in retinal images, cell segmentation [9]–[11] in electron microscopic (EM) recordings, lung segmentation [12]–[16] and brain segmentation [17]–[”
• The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, background): “Although segmentation methods for medical images have been a hot spot of concern for a long time, there is still a long way to detect a fully automated application [59-61].”
• Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “Further, for the task of automatic tumor segmentation, CNNs have been applied to segmentation of tumors in brain [5], [12], [13], liver [14], breast [15], lung [16], [17],”