ESTRO 2022 - Abstract Book

S1447

Abstract book

ESTRO 2022

Conclusion A large number of lung SBRT plans was re-evaluated using our 3D SDC at the same criteria as for pretreatment measurements. The majority of plans (95%) passed these criteria. It is often recommended to do extra QA for plans that are more complex than the “standard” plans. However, by using (monitor units)/(prescribed dose) as plan complexity measure, 3 plans would have escaped extra QA. By using tighter 3D SDC criteria potentially critical plans can be identified. We recommend a risk-based selection for pretreatment QA by measuring those plans that do not meet the pretreatment criteria in the 3D SDC and to combine this with sampling the rest of the plans. This will result in a vast reduction of work load.

PO-1650 Hippocampus delineation by a neural network and CT-brain projections.

A.T. Hansen 1

1 Århus University Hospital, Department of Oncology, Medical Physics, Aarhus , Denmark

Purpose or Objective The aim is to demonstrate the ability of a neural network based on the TensorFlow software to perform automatic delineation of the hippocampus based only on lateral and frontal projections of the brain contour obtained from a CT-scan. Materials and Methods The computer used was a 64 bits Intel Core i5 desktop computer with Python 3.8.3 and TensorFlow version 2.2.0 installed. The neural structure used consisted of three stages. First three dense layers with a total of 3940 neurons. Then a reshaping of the data, and finally a transposed convolutional layer that generated the output image. The data basis was the delineated brain from CT-scans and the left hippocampus from MR-scans of 16 brain patients. Several Python programs were designed to do the following three types of data processing. Training data From the data basis the training data was created as a multitude of image triplets consisting of two input images and one output image. These triplets were made by rotating and longitudinally translating the 3D contours of the brain and left hippocampus in a multitude of ways. And register frontal (x-z) and lateral (y-z) projections of the brain and the corresponding transversal (x-y, z=0) contours of the left hippocampus. The brain projections were pooled to 100 × 100 pixels, smoothed and contrast enhanced. The hippocampus contour was filled it had a size of 56 × 56 pixels. (see figure 1, top row). The final number of training image triplets were 83871. Training The neural network was trained (fitted) to the relation between the input brain projections and the output hippocampus contour. The network was fitted for 2000 iterations which took 80 seconds each, close to two days in total. Prediction The trained neural network can predict a 2D hippocampus contour from two input projections (see figure 1, bottom). For an unknown patient several predictions can be joined to create a 3D hippocampus contour. This contour can be written in DICOM to be exported to the Eclipse treatment planning system.