The purpose of this study was to evaluate radiologists’ performance in detecting lung nodules using chest computed tomography (CT) scans when assisted by a computer-aided detection (CAD) system with a vessel suppression function. Three radiologists participated in this preliminary observer study. The observer study was conducted on 80 CT scans including 94 nodules. The ratio of nodule-free scans to with-nodule scans was 1:1. CAD systems with (CAD-VS) and without (CAD-nVS) a vessel suppression function were developed to assist radiologists in reading chest CT scans. The radiologists read the CT scans in a two-session process, which had at least a one-month interval in between. Freeresponse receiver operating characteristic (FROC) curves and localization receiver operating characteristic (LROC) curves were utilized to analyze the nodule detection results. The CAD-VS and the CAD-nVS detected 96.8% and 93.6% of nodules, respectively, at 0.5 false positive per scan. For the observer study, the mean area under the LROC curve (LROC-AUC) for nodule detection improved from 0.877 by use of the CAD-nVS to 0.942 by use of the CAD-VS. Radiologists averagely detected 94.0% and 96.5% of nodules with the CAD-nVS and CAD-VS, respectively; average specificity increased from 71.7% to 81.7%. The CAD-VS improved radiologists’ performance for lung nodule detection, compared to the general CAD-nVS. This suggests that the CAD-VS technique is feasible to help radiologists further improve the clinical detection accuracy of lung nodules in chest CT scans.
Convolutional neural networks (CNNs) have been increasingly applied to computer-aided diagnosis (CADx) for lung nodule malignancy prediction, which usually is a binary classification task. However, CNNs were often difficult to capture optimal features, thereby affect the classification performance. This study developed a CADx system based on a CNN model with auxiliary task learning to predict lung nodule malignancy in chest computed tomography (CT) scans. Our CADx system took raw CT image cubes centering at nodules as input and generated one main output and eight auxiliary outputs. The main output predicted lung nodule malignancy; the auxiliary outputs predicted lesion size and characteristics. The auxiliary tasks offered assistance for predicting the final nodule malignancy. The performance of the developed lung nodule CADx system was verified by use of the LIDC dataset. Results showed that our CADx system achieved improved performance for lung nodule malignancy prediction.
The suppression of lung vessels in chest computed tomography (CT) scans can enhance the conspicuity of lung nodules, thereby may improve the detection rate of early lung cancer. This study aimed to verify the effect of lung vessel suppression on the performance of the lung nodule detector. Firstly, a lung vessel suppression technique was developed to remove the vessels while preserving the nodules. Then, a lung nodule detector was developed with two stages: nodule candidate generation and false positive reduction. The vessel suppression and nodule detection methods were validated respectively in 50 three-dimensional (3D) chest CT images with manually-labeled vessel trees and 888 3D chest CT images with manually-located nodules (LUNA16). The lung vessel suppression results were quantitatively evaluated by using the Dice coefficient (DICE) and the contrast-to-noise ratio (CNR), and the lung nodule detection results were quantitatively evaluated by using the sensitivity under two conditions: “without” and “with vessel suppression”. The lung vessel suppression accurately removed vessels with a DICE of 0.943 and improved the CNR for nodules from 4.24 (6.27 dB) to 7.02 (8.46 dB), which subsequently improved the average sensitivity from 0.948 to 0.969 under 7 specified false positives for lung nodule detection.
Temporal subtraction of sequential chest radiographs based on image registration technique has been developed for decades to assist radiologists in the detection of interval changes. Although the performance of current methods is good, the computation cost of these methods is generally high. The high computation cost is mainly due to the iterative optimization problem of non-learning-based deformable registration. In this work we present a fast unsupervised learning-based algorithm for deformable registration of chest radiographs. Based on a convolutional neural network, the proposed model learns to directly estimate spatial transformations from pairs of moving images and fixed images, and uses the transformations to warp the moving images. We apply a regularization term to constrain the model to learn local matching. The model is trained by optimizing a pair-wise similarity metric between the warped moving image and the fixed image, with no need for any supervised information such as ground truth deformation fields. The trained model can be used to predict the warped moving images in one shot, and is thus very fast. The subtraction images of the warped images and the fixed images are able to enhance various interval changes. The preliminary results showed that for approximately 98.55% cases, the learning-based method could obtain improved or comparable registration in comparison with the baseline method.
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