Deep convolutional neural networks for multiplanar lung nodule detection: Improvement in small nodule identification.

ABSTRACT:

Purpose

Early detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes.

Methods

The nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the thr

SUBMITTER: Zheng S 

PROVIDER: S-EPMC7986069 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature