Abstract
The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.
Highlights
(in the range between −750 and −300 HU), referred to as ground glass opacities
In a scenario in which computer-aided diagnosis (CAD) systems are used to automate the lung cancer screening workflow from nodule detection to automatic report with decision on nodule workup, it is necessary to solve the problem of automatic classification of nodule type
All Computed Tomography (CT) scans were first read by a workstation (CIRRUS Lung Screening, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, Netherlands) with automatic nodule detection (CAD) tools integrated
Summary
(in the range between −750 and −300 HU), referred to as ground glass opacities. In a scenario in which CAD systems are used to automate the lung cancer screening workflow from nodule detection to automatic report with decision on nodule workup, it is necessary to solve the problem of automatic classification of nodule type In this context, the classes that have to be considered are: (i) solid, (ii) non-solid, (iii) part-solid, (iv) calcified, (v) perifissural and (vi) spiculated nodules. 13, which was used to assess presence of spiculation in detected solid nodules[14] and to classify nodules as perifissural[15] This approach could be extended to other nodule types, it strongly relies on the estimation of nodule size in order to define the proper scale to analyze data. The most used incarnation of deep neural networks are convolutional networks[16,18,19], a supervised learning algorithm suited to solve problems of classification of natural images[19,20,21], which has recently been applied to some applications in chest CT analysis[6,15,22,23,24]
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