  Contouring of the target volume and Organs-At-Risk (OARs) is a crucial step
in radiotherapy treatment planning. In an adaptive radiotherapy setting,
updated contours need to be generated based on daily imaging. In this work, we
leverage personalized anatomical knowledge accumulated over the treatment
sessions, to improve the segmentation accuracy of a pre-trained Convolution
Neural Network (CNN), for a specific patient. We investigate a transfer
learning approach, fine-tuning the baseline CNN model to a specific patient,
based on imaging acquired in earlier treatment fractions. The baseline CNN
model is trained on a prostate CT dataset from one hospital of 379 patients.
This model is then fine-tuned and tested on an independent dataset of another
hospital of 18 patients, each having 7 to 10 daily CT scans. For the prostate,
seminal vesicles, bladder and rectum, the model fine-tuned on each specific
patient achieved a Mean Surface Distance (MSD) of $1.64 \pm 0.43$ mm, $2.38 \pm
2.76$ mm, $2.30 \pm 0.96$ mm, and $1.24 \pm 0.89$ mm, respectively, which was
significantly better than the baseline model. The proposed personalized model
adaptation is therefore very promising for clinical implementation in the
context of adaptive radiotherapy of prostate cancer.
