A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation

TL;DR

This paper develops a 3D CNN model for lung nodule proposal generation, analyzes training and platform efficiency, and introduces an optimized CPU-based framework to enhance 3D medical image processing.

Contribution

It presents a state-of-the-art 3D CNN model for lung nodule proposals and introduces a CPU-optimized framework for efficient 3D computations.

Findings

High-resolution training improves small nodule detection.

CPU platforms offer larger memory for 3D applications.

Proposed framework enhances 3D computation efficiency.

Abstract

Lung nodule proposals generation is the primary step of lung nodule detection and has received much attention in recent years . In this paper, we first construct a model of 3-dimension Convolutional Neural Network (3D CNN) to generate lung nodule proposals, which can achieve the state-of-the-art performance. Then, we analyze a series of key problems concerning the training performance and efficiency. Firstly, we train the 3D CNN model with data in different resolutions and find out that models trained by high resolution input data achieve better lung nodule proposals generation performances especially for nodules in too small sizes, while consumes much more memory at the same time. Then, we analyze the memory consumptions on different platforms and the experimental results indicate that CPU architecture can provide us with larger memory and enables us to explore more possibilities of 3D applications. We implement the 3D CNN model on CPU platform and propose an Intel Extended-Caffe framework which supports many highly-efficient 3D computations, which is opened source at https://github.com/extendedcaffe/extended-caffe.