2.75D Convolutional Neural Network for Pulmonary Nodule Classification in Chest CT

by   Ruisheng Su, et al.

Early detection and classification of pulmonary nodules in Chest Computed tomography (CT) images is an essential step for effective treatment of lung cancer. However, due to the large volume of CT data, finding nodules in chest CT is a time consuming thus error prone task for radiologists. Benefited from the recent advances in Convolutional Neural Networks (ConvNets), many algorithms based on ConvNets for automatic nodule detection have been proposed. According to the data representation in their input, these algorithms can be further categorized into: 2D, 3D and 2.5D which uses a combination of 2D images to approximate 3D information. Leveraging 3D spatial and contextual information, the method using 3D input generally outperform that based on 2D or 2.5D input, whereas its large memory footprints becomes the bottleneck for many applications. In this paper, we propose a novel 2D data representation of a 3D CT volume, which is constructed by spiral scanning a set of radials originated from the 3D volume center, referred to as the 2.75D. Comparing to the 2.5D, the 2.75D representation captures omni-directional spatial information of a 3D volume. Based on 2.75D representation of 3D nodule candidates in Chest CT, we train a convolutional neural network to perform the false positive reduction in the nodule detection pipeline. We evaluate the nodule false positive reduction system on the LUNA16 data set which contains 1186 nodules out of 551,065 candidates. By comparing 2.75D with 2D, 2.5D and 3D, we show that our system using 2.75D input outperforms 2D and 2.5D, yet slightly inferior to the systems using 3D input. The proposed strategy dramatically reduces the memory consumption thus allow fast inference and training by enabling larger number of batches comparing to the methods using 3D input.


False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks

Recent studies have shown that lung cancer screening using annual low-do...

Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics

Early detection of lung cancer is essential in reducing mortality. Recen...

Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging

Detection of pulmonary nodules in chest CT imaging plays a crucial role ...

Automated pulmonary nodule detection using 3D deep convolutional neural networks

Early detection of pulmonary nodules in computed tomography (CT) images ...

Lymph Node Gross Tumor Volume Detection in Oncology Imaging via Relationship Learning Using Graph Neural Network

Determining the spread of GTV_LN is essential in defining the respective...

Please sign up or login with your details

Forgot password? Click here to reset