WE-E-213CD-09: Multi-Atlas Fusion Using a Tissue Appearance Model

Retrospective Validation and Clinical Implementation of Automated Contouring of Organs at Risk in the Head and Neck: A Step Toward Automated Radiation Treatment Planning for Low- and Middle-Income Countries

Purpose

We assessed automated contouring of normal structures for patients with head-and-neck cancer (HNC) using a multiatlas deformable-image-registration algorithm to better provide a fully automated radiation treatment planning solution for low- and middle-income countries, provide quantitative analysis, and determine acceptability worldwide.

Methods

Autocontours of eight normal structures (brain, brainstem, cochleae, eyes, lungs, mandible, parotid glands, and spinal cord) from 128 patients with HNC were retrospectively scored by a dedicated HNC radiation oncologist. Contours from a 10-patient subset were evaluated by five additional radiation oncologists from international partner institutions, and interphysician variability was assessed. Quantitative agreement of autocontours with independently physician-drawn structures was assessed using the Dice similarity coefficient and mean surface and Hausdorff distances. Automated contouring was then implemented clinically and has been used for 166 patients, and contours were quantitatively compared with the physician-edited autocontours using the same metrics.

Results

Retrospectively, 87% of normal structure contours were rated as acceptable for use in dose-volume-histogram–based planning without edit. Upon clinical implementation, 50% of contours were not edited for use in treatment planning. The mean (± standard deviation) Dice similarity coefficient of autocontours compared with physician-edited autocontours for parotid glands (0.92 ± 0.10), brainstem (0.95 ± 0.09), and spinal cord (0.92 ± 0.12) indicate that only minor edits were performed. The average mean surface and Hausdorff distances for all structures were less than 0.15 mm and 1.8 mm, respectively.

Conclusion

Automated contouring of normal structures generates reliable contours that require only minimal editing, as judged by retrospective ratings from multiple international centers and clinical integration. Autocontours are acceptable for treatment planning with no or, at most, minor edits, suggesting that automated contouring is feasible for clinical use and in the ongoing development of automated radiation treatment planning algorithms.

Automatic segmentation of cardiac substructures from noncontrast CT images: accurate enough for dosimetric analysis?

PURPOSE

We evaluated the feasibility of using an automatic segmentation tool to delineate cardiac substructures from noncontrast computed tomography (CT) images for cardiac dosimetry and toxicity analyses for patients with nonsmall cell lung cancer (NSCLC) after radiotherapy.

MATERIAL AND METHODS

We used an in-house developed multi-atlas segmentation tool to delineate 11cardiac substructures, including the whole heart, four heart chambers, and six greater vessels, automatically from the averaged 4D-CT planning images of 49 patients with NSCLC. Two experienced radiation oncologists edited the auto-segmented contours. Times for automatic segmentation and modification were recorded. The modified contours were compared with the auto-segmented contours in terms of Dice similarity coefficient (DSC) and mean surface distance (MSD) to evaluate the extent of modification. Differences in dose-volume histogram (DVH) characteristics were also evaluated for the modified versus auto-segmented contours.

RESULTS

The mean automatic segmentation time for all 11 structures was 7-9 min. For the 49 patients, the mean DSC values (±SD) ranged from .73 ± .08 to .95 ± .04, and the mean MSD values ranged from 1.3 ± .6 mm to 2.9 ± 5.1 mm. Overall, the modifications were small; the largest modifications were in the pulmonary vein and the inferior vena cava. The heart V30 (volume receiving dose ≥30 Gy) and the mean dose to the whole heart and the four heart chambers were not different for the modified versus the auto-segmented contours based on the statistically significant condition of p < .05. Also, the maximum dose to the great vessels was no different except for the pulmonary vein.

CONCLUSIONS

Automatic segmentation of cardiac substructures did not require substantial modifications. Dosimetric evaluation showed no significant difference between the auto-segmented and modified contours for most structures, which suggests that the auto-segmented contours can be used to study cardiac dose-responses in clinical practice.

Cardiac atlas development and validation for automatic segmentation of cardiac substructures

PURPOSE

To develop and validate a set of atlases for auto-contouring cardiac substructures.

METHODS

Eight radiation oncologists manually and independently delineated 15 cardiac substructures from noncontrast CT images of 6 patients by referring to their respective fused contrast CT images. Individual contours were fused together for each structure, edited by 2 physicians, and became atlases to delineate other 6 patients. The auto-delineated contours of the 6 additional patients became templates for manual contouring. These 12 patients with well-defined contours composed the final atlases for multi-atlas segmentation.

RESULTS

The average time for manually contouring the 15 cardiac substructures was about 40min. Inter-observer variability was small for the heart, the chambers, and the aorta compared with that for other structures that were not clearly distinguishable in CT images. The mean dice similarity coefficient and mean surface distance of auto-segmented contours were within one standard deviation of expert contouring variability. Good agreement between auto-segmented and manual contours was observed for the heart, the chambers, and the great vessels. Independent validation on other 19 patients showed reasonable agreement for the heart chambers.

CONCLUSIONS

A set of cardiac atlases was created for auto-contouring from noncontrast CT images. The accuracy of auto-contouring for the heart, chambers, and great vessels was validated for potential clinical use.