ESTRO 36 Abstract Book

S549 ESTRO 36 2017 _______________________________________________________________________________________________

PO-1005 Automatic segmentation of cardiac sub- structures in the treatment of HL C. Fiandra 1 , M. Levis 1 , F. Cadoni 1 , V. De Luca 1 , F. Procacci 1 , A. Cannizzaro 1 , R. Ragona 1 , U. Ricardi 1 1 University of Torino, Oncology, Torino, Italy Purpose or Objective to validate, in the context of treatment of Hodgkin Lymphoma, three commercial software solutions for atlas- based segmentation of cardiac sub-structures Material and Methods 25 patients were selected and then divided into two groups: 15 patients will make up the personalized atlas and 10 patients on which will be applied atlases created in order to assess its quality. For the selection of patients, the following inclusion criteria were selected: patients with HL presentation of a mediastinal mass at the onset of the disease and the availability of CT imaging with contrast. Two expert physicians have delineated on the diagnostic CT with contrast the selected 15 patients cardiac structures: the heart as a whole, the four chambers of the heart, the coronary artery and valvular structures; which will compose the atlas. We use three commercial solutions (Velocity AI, MIM and RayStation) in order to compare their results; the structures delineated by doctors on the 5 control patients will be compared with those automatically drawn by atlases, through the conformality function (Dice Index (DI)). In addition, the atlases underwent a clinical evaluation of the involved physicians: in particular it was asked to a Radiation Oncologist to analyze contours made by the three software on reference patients to evaluate the goodness of the warp made from atlases than those performed by him. Clinical judgments were recorded on a scale of numerical values: 1 = poor; 2 = medium; 3 = good. Results in terms of statistical analysis, the data obtained from the values of Dice Index were compared structure by structure between the three platforms. The Figure 1 shows only structures with a Dice Index more than 0.5 (right atrium, left atrium, the heart, the left side wall, interventricular septum, aortic valve, left ventricle and right ventricle). The differences between the 3 software were calculated and the structures delineated by MIM have more frequently higher values of Dice Index, compared to those of Velocity and RayStation, with respectively 0.03 to 0.01 p-value. Instead the difference between Velocity and RayStation is not statistically significant (p-value = 0.8). Regarding the evaluation of the Radiation Oncologist as compared to DI, values show that RayStation is the software that realizes contours more applicable in clinical practice, with statistically significant differences from Velocity and MIM, with p-value respectively of 0.038 and 0.046. While the difference between Velocity and MIM is not statistically significant (p-value = 0.083). Conclusion In general we can say that the contours applied by atlases are valid, even if not yet optimal and they may represent a starting point for the step of contouring, useful to speed up this process; based on the values of Dice Index collected in this study, MIM performs better while RayStation appears the most powerful software from a clinical point of view thus obtaining contours more

was observed in area of Hippocampus (less than 5 mm).

Conclusion Conclusion : With complete radiological diagnosis, HA- WBRT can be delivered with oncological safety. Proper margin definition of HA in delineation is to be confirmed with individual technique. PO-1004 Machine learning methods for automated OAR segmentation P. Tegzes 1 , A. Rádics 1 , E. Csernai 1 , L. Ruskó 2 1 General Electric, Healthcare, Budapest, Hungary 2 General Electric, Healthcare, Szeged, Hungary Purpose or Objective Manual contouring of organs at risk can take significant time. The aim of this project is to use machine learning to develop fully automated algorithms to delineate various organs in the head and neck region on CT images. Material and Methods Machine learning models were built based on 48 CT sequences of the head and neck region with 5 manually contoured organs from the Public Domain Database for Computational Anatomy. Data were randomly separated to 32 train, 8 cross-validation and 8 test sequences. Three different machine learning models were combined to achieve automated segmentation. The first step uses a support vector machine classifier to separate patient anatomy from all other objects (a). The second step applies slice-based deep learning classification to detect the bounding box around the organ of interest (b). The final step achieves voxel-level classification based on a fully connected neural net on the voxel intensities of suitably selected neighboring voxels (c). Very similar model architectures were trained for all the different organs.

Results The body contour detection has been previously trained on another dataset containing full-body images and achieved an average accuracy of 96.6%. The mean error of the bounding box edges was 3mm, the corresponding dice scores ranged from 72% to 94% depending on the organ of interest. The first results of the voxel level segmentation gave average dice values of 38% to 77% depending on the investigated organ, and several opportunities for further fine-tuning have been identified. Conclusion Machine learning methods can be competitive with standard image processing algorithms in the field of organ segmentation.