Multi-view multi-scale CNNs for lung nodule type classification from CT images

作者：

Highlights：

• A comprehensive method for classifying not only solid nodule types such as well-circumscribed and vascularized ones, but also GGO and non-nodule types.

• A normalized spherical sampling pattern based on icosahedron and a nodule radius approximation method based on thresholding.

• A better view selection method for nodules on CT images based on high frequency content analysis.

• A multi-scale multi-view re-sampling and color projection method for n- odules, based on which the CNNs with maximum pooling is trained.

• A comprehensive validation on the publicly accessible datasets of LIDC- IDRI and ELCAP.

摘要

•A comprehensive method for classifying not only solid nodule types such as well-circumscribed and vascularized ones, but also GGO and non-nodule types.•A normalized spherical sampling pattern based on icosahedron and a nodule radius approximation method based on thresholding.•A better view selection method for nodules on CT images based on high frequency content analysis.•A multi-scale multi-view re-sampling and color projection method for n- odules, based on which the CNNs with maximum pooling is trained.•A comprehensive validation on the publicly accessible datasets of LIDC- IDRI and ELCAP.

论文关键词：Computed tomography,Lung nodule,CNNs

论文评审过程：Received 26 March 2017, Revised 13 December 2017, Accepted 30 December 2017, Available online 4 January 2018, Version of Record 16 January 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.12.022