Abstract
3D visualization diagnosis for pulmonary nodule detection and segmentation is becoming a promising topic in the field of surgical researches and applications. Aiming at assisting radiologists to diagnose pulmonary nodules more accurately, the methods of detection and segmentation for pulmonary nodule 3D visualization diagnosis were proposed based on Mask Region-Convolutional Neural Network (Mask R-CNN) and ray-casting volume rendering algorithm. The Mask R-CNN used resnet50 as the backbone and applied Feature Pyramid Network (FPN) to fully explore multiscale feature maps. And then, Region Proposal Network (RPN) was used to propose candidate bounding boxes. Furthermore, the mask matrices and the raw medical image sequences were multiplied to obtain sequences of predicted pulmonary nodules. Finally, ray-casting volume rendering algorithm was applied to generate the 3D models of pulmonary nodules. The proposed methods are tested and evaluated on publicly available LUNA16 dataset and the independent dataset from Ali TianChi challenge. Experimental results show that Mask R-CNN of weighted loss reaches sensitivities of 88.1% and 88.7% at 1 and 4 false positives per scan, respectively. Meanwhile, we can obtain AP@50 score of 0.882 using Mask R-CNN with weighted loss on labelme_LUNA16 dataset, which outperforms many existing state-of-the-art approaches of detection and segmentation of pulmonary nodules.
Highlights
According to the latest report of the World Health Organization [1], cancer is the second leading cause of death globally and is responsible for estimated 9.6 million deaths in 2018
The Lung Image Database Consortium (LIDC) was combined with the Infectious Disease Research Institute (IDRI) in 2009 which created the LIDC-IDRI database, including 1018 chest computed tomography (CT) scans and annotations made by four radiologists on each scan
We proposed a pulmonary nodule detection, segmentation and 3D visualization system, of which the detection and segmentation were based on the Mask R-CNN and the 3D visualization was based on the volume rendering algorithm of ray-casting
Summary
According to the latest report of the World Health Organization [1], cancer is the second leading cause of death globally and is responsible for estimated 9.6 million deaths in 2018. Among these deadly cancers, lung cancer is the most common cancer which has 2.09 million cases and the most common cause of cancer death which has 1.76 million deaths. One of the commonly used methods to diagnose the early form of lung cancer (i.e. pulmonary nodules) is analyzing medical image of chest. Speaking, computed tomography (CT) is the most accurate imaging mode for lung disease detection [3]. A whole chest CT case usually contains hundreds of two-dimensional
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