The overall goal of this work is to develop a rapid, accurate and fully automated software tool to estimate patient-specific organ doses from CT scans using a deterministic Linear Boltzmann Transport Equation (LBTE) solver (Acuros CTD) and deep-learning CT segmentation algorithms. This study evaluated the accuracy of deep learning segmentation for estimating organ dose from dose maps generated by Acuros. The study focused on pediatric CT due to increased radiation concerns and segmentation challenges for the pediatric population. Organs relevant to CT dosimetry were manually contoured by experts in 246 pediatric chest-abdomen-pelvis CT datasets to serve as ground truth. A fully convolutional network based on a modified V-net architecture was trained and tuned using 226 pediatric datasets ranging in age from 1 to 16 years. An additional twenty datasets were used for preliminary evaluation. The accuracy of organ dose estimates obtained from deep learning segmentation was evaluated relative to doses obtained from the ground truth contours. The deep learning segmentation algorithm resulted in low dose errors for all organs, with a mean absolute error across test patients of 1% or less and a maximum error of 3.5% for the heart. There was high similarity between deep learning and expert contours, with mean Dice coefficients across patients greater than or equal to 0.95. There was no correlation between organ dose error or Dice coefficient with the patient age. Based on statistical analysis of students paired T-test, there was no statistically significant difference between organ doses estimated using the deep learning contours as compared to the expert ground truth contours (p>0.2). Overall, the deep learning segmentation models applied to dose maps generated by the LBTE solver (Acuros CTD) resulted in high organ dose accuracy. Additional evaluation is planned for more organ structures and patient datasets.