We propose a learning-based method to automatically segment extraprostatic nodal lesion from 18F-fluciclovine (anti-1- amino-3-[18F] fluorocyclobutane-1-carboxylic acid) PET images. Our proposed method, named hierarchical activation network, consists of three main subnetworks: a fully convolutional one-stage object detection (FCOS) network and a mask module, and a hierarchical convolutional block. While FCOS is employed to detect the view-of-interests (VOIs) of extraprostatic nodal lesion. Hierarchical convolutional block is used to derive activation map to boost the classification accuracy around lesion boundary. This is followed by the binary segmentation of extraprostatic nodal lesion within the detected VOI by mask module. To evaluate the proposed method, we retrospectively investigated 92 lesions with 18F- fluciclovine PET acquired. On each dataset, the extraprostatic lesions were delineated by physicians and was served as ground truth and training target. The proposed method was trained and evaluated by a five-fold cross validation strategy. The average DSC among all lesions is close to 0.7. The proposed method has great potential in improving the efficiency and mitigating the observer-dependence in extraprostatic lesion contouring for radiation therapy.
CT is routinely used for radiotherapy planning with organs and regions of interest being segmented for diagnostic evaluation and parameter optimization. For cardiac segmentation, many methods have been proposed for left ventricular segmentation, but few for simultaneous segmentation of the entire heart. In this work, we present a convolutional neural networks (CNN)-based cardiac chamber segmentation method for 3D CT with 5 classes: left ventricle, right ventricle, left atrium, right atrium, and background. We achieved an overall accuracy of 87.2% ± 3.3% and an overall chamber accuracy of 85.6 ± 6.1%. The deep learning based segmentation method may provide an automatic tool for cardiac segmentation on CT images.
Cardiovascular disease is a leading cause of death in the United States. The identification of cardiac diseases on conventional three-dimensional (3D) CT can have many clinical applications. An automated method that can distinguish between healthy and diseased hearts could improve diagnostic speed and accuracy when the only modality available is conventional 3D CT. In this work, we proposed and implemented convolutional neural networks (CNNs) to identify diseased hears on CT images. Six patients with healthy hearts and six with previous cardiovascular disease events received chest CT. After the left atrium for each heart was segmented, 2D and 3D patches were created. A subset of the patches were then used to train separate convolutional neural networks using leave-one-out cross-validation of patient pairs. The results of the two neural networks were compared, with 3D patches producing the higher testing accuracy. The full list of 3D patches from the left atrium was then classified using the optimal 3D CNN model, and the receiver operating curves (ROCs) were produced. The final average area under the curve (AUC) from the ROC curves was 0.840 ± 0.065 and the average accuracy was 78.9% ± 5.9%. This demonstrates that the CNN-based method is capable of distinguishing healthy hearts from those with previous cardiovascular disease.
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