Use of a convolutional neural network (CNN) to determine if the patient’s eye lens is in the beam for x-ray image projections

The eye lens is a very radiosensitive organ and is at risk for cataractogenesis during neuro-interventional procedures. It is paramount that the lens is exposed to the x-ray beam as little as possible while still being able to complete the clinical task. In this preliminary investigation, a convolutional neural network (CNN) has been created in order identify if the lens is within the x-ray projection image and where it is located with the intent to facilitate lens dose estimation. The model was trained using a database of patient cases of radiographic skull images, which had different views, in order to generalize the data. The size of the dataset was increased by rotating the images at various angles and masks were created for each corresponding image by hand-contouring the eye socket in the image. For image segmentation, a U-Net model was used which consisted of a down-block, bottleneck, and up-block. Different network parameters were tested and receiver operating curves (ROCs), with Jaccard indices, were assessed to identify the best model. The end goal of this project is model implementation into the real-time Canon Dose Tracking System (DTS) during interventional fluoroscopic procedures. This will allow the DTS to have a more accurate identification of where the lens is, whether fully in the beam or only partially. With this information, a more accurate calculation of the eye lens dose can be done which allows for patients’ dose to be more carefully monitored.