GPD-Nodule: A Lightweight Lung Nodule Detection and Segmentation Framework on Computed Tomography Images Using Uniform Superpixel Generation

Abstract

Lung nodule detection is key in early diagnosis of lung cancer. Expert radiologists dedicate a significant amount of time and effort to detecting such nodules manually by going through computed tomography scan images slice by slice. This endeavor results in the slow processing of radiological images and possible misdiagnosis due to nodules being tiny by nature. In this paper, we introduce a two-step automatic computer-aided nodule detection method that encompasses a novel uniform superpixel generation algorithm, namely, equivalent patchwise iterative agglomerative clustering. This superpixel generation algorithm can generate the same number of superpixels for every image making it suitable for training neural networks. This method is then coupled with a novel variant of graph neural networks, namely, the curtailed residual nested superpixel propagation network, and an unsupervised region proposal method, namely, pixel nesting region proposal mechanism to detect nodules with high accuracy. The results show an accelerated training process compared to state-of-the-art convolutional neural networks and good generalization capability. Furthermore, the proposed method displays a significant reduction in trainable parameters while achieving high performance in the detection and segmentation of nodules on the Lung Image Database Consortium and Image Database Resource Initiative dataset.