Tumor Doubling Time Using CT Volumetric Segmentation in Metastatic Adrenocortical Carcinoma

Adrenocortical carcinoma (ACC) is a rare malignancy with an overall unfavorable prognosis. Clinicians treating patients with ACC have noted accelerated growth in metastatic liver lesions that requires rapid intervention compared to other metastatic locations. This study measured and compared the growth rates of metastatic ACC lesions in the lungs, liver, and lymph nodes using volumetric segmentation. A total of 12 patients with metastatic ACC (six male; six female) were selected based on their medical history. Computer tomography (CT) exams were retrospectively reviewed and a sampling of ≤5 metastatic lesions per organ were selected for evaluation. Lesions in the liver, lung, and lymph nodes were measured and evaluated by volumetric segmentation. Statistical analyses were performed to compare the volumetric growth rates of the lesions in each organ system. In this cohort, 5/12 had liver lesions, 7/12 had lung lesions, and 5/12 had lymph node lesions. A total of 92 lesions were evaluated and segmented for lesion volumetry. The volume doubling time per organ system was 27 days in the liver, 90 days in the lungs, and 95 days in the lymph nodes. In this series of 12 patients with metastatic ACC, liver lesions showed a faster growth rate than lung or lymph node lesions.

3D Quantum-inspired Self-supervised Tensor Network for Volumetric Segmentation of Medical Images

<div>This paper introduces a novel shallow 3D self-supervised tensor neural network for volumetric segmentation of medical images with merits of obviating training and supervision. The proposed network is referred to as 3D Quantum-inspired Self-supervised Tensor Neural Network (3D-QNet). The underlying architecture of 3D-QNet is composed of a trinity of volumetric layers viz. input, intermediate and output layers inter-connected using an S-connected third-order neighborhood-based topology for voxel-wise processing of 3D medical image data suitable for semantic segmentation. Each of the volumetric layers contains quantum neurons designated by qubits or quantum bits. The incorporation of tensor decomposition in quantum formalism leads to faster convergence of the network operations to preclude the inherent slow convergence problems faced by the classical supervised and self-supervised networks. The segmented volumes are obtained once the network converges. The suggested 3D-QNet is tailored and tested on the BRATS 2019 Brain MR image data set and Liver Tumor Segmentation Challenge (LiTS17) data set extensively in our experiments. 3D-QNet has achieved promising dice similarity as compared to the intensively supervised convolutional network-based models like 3D-UNet, Vox-ResNet, DRINet, and 3D-ESPNet, showing a potential advantage of our self-supervised shallow network on facilitating semantic segmentation.</div>

Highly accurate optical flow method based on volumetric segmentation for 3D PIV

In this study, we present a new three-dimensional optical flow method based on volumetric segmentation for the velocity estimation of fluid flow. The proposed method uses a segmented smoothness term that is designed on the assumption that the particle velocity varies continuously in each segmented volume and discontinuously on the surfaces of the segmented volumes. Subsequently, the data term is proposed on the basis of the segmented volumes and the fluid mass conservation equation, which is derived from the Reynolds transport equation. In addition, the robust local level-set method is applied to segment the particle volume according to the velocity distribution of fluid flow. The proposed method is evaluated quantitatively on synthetic data and qualitatively on experimental data, and the velocity results are compared to the advanced 3D velocity estimation methods. The results indicate that the proposed method can obtain velocity fields with greater measurement accuracy for Tomo-PIV.