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[en] A rigorous method for automated soft tissue segmentation using planar kilovoltage (kV) imaging, a photon counting detector (PCD), and a convolutional neural network is presented. The goal of the project was to determine the optimum number of energy bins in a PCD for soft tissue segmentation. Planar kV X-ray images of solid water (SW) phantoms with varying depth of cartilage were generated with a cone-beam analytical method and parallel-beam Monte Carlo simulations. Simulations were preformed using 2 to 5 PCD energy bins with equal photon fluence distribution. Simulated image signal to noise ratio (SNR) was varied between 10 to 250 measured after transmission through 4 cm of SW. Algorithms using non-linear as well as linear regression were used to predict the amount of cartilage for every pixel of the phantom. These algorithms were evaluated based on the mean squared error (MSE) between their prediction and the ground truth. The best algorithm was used to decompose randomly generated SW and cartilage images with an SNR of 100. These randomly generated images trained a U-Net convolutional neural network to segment the cartilage in the image. The results indicated the smallest MSE occurred for non-linear regression with 4 energy bins over all SNR. The trained U-Net was able to correctly segment all regions of cartilage for the smallest amount of cartilage used (4 mm) and segmented the region with > 99% categorical accuracy by pixel.