| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 107 === In the procedures of radiotherapy, delineating the organ at risk (OAR) is a time-consuming, laborious but still very important task, which is necessary to be done accurately.
In the field of medical imaging analysis, the content of images has high complexity and variability. The traditional rule-based methods are often difficult to meet the clinical requirements. In this work, we study the deep learning segmentation techniques and apply them to clinical imaging systems.
In the chapter of experimentation, we used public liver organ datasets, 3Dircadb, to verify various types of segmentation models. By the inspiration of these models we proposed a fusion model that uses the Convolutional LSTM Layer to study the spatial correlation between layers in a CT image dataset. We also use Attention Mechanism to suppress irrelevant features from the complex image content and focus on the useful messages of target organs. Finally, this model is verified in the testing dataset and achieves the highest Dice score. In addition, the 2015 MICCAI public data set on organ segmentation was used to segment the stomach. We show that the proposed fusion model still has the best performance even when the organ boundaries are more difficult to discriminate.
The last experimental results show that the diversity of training dataset used by deep learning techniques is very important in clinical application. For patients with special disease conditions, extra data with similar characteristics is required to make the prediction more accurate. Pre-trained model parameters can improve the results of testing, but the results are poor when directly applied to test a different dataset.
We also proposed three feasible solutions for practically applying deep learning methods in radiotherapy. It allows us to successfully write the predicted contour results into DICOM-RT format, which is a standard data format compatible with most medical imaging systems. Therefore the clinician could directly fine-tune the predicted contour and save the time for other clinical work.
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