Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning

Accounting for uncertainties in the position of anatomical barriers used to define the clinical target volume

The definition of the clinical target volume (CTV) is becoming the weakest link in the radiotherapy chain. CTV definition consensus guidelines include the geometric expansion beyond the visible gross tumor volume, while avoiding anatomical barriers. In a previous publication we described how to implement these consensus guidelines using deep learning and graph search techniques in a computerized CTV auto-delineation process. In this paper we address the remaining problem of how to deal with uncertainties in positions of the anatomical barriers. The objective was to develop an algorithm that implements the consensus guidelines on considering barrier uncertainties. Our approach is to perform multiple expansions using the fast marching method with barriers in place or removed at different stages of the expansion. We validate the algorithm in a computational phantom and compare manually generated with automated CTV contours, both taking barrier uncertainties into account.

[Siriade 2.0: An e-learning platform for radiation oncology contouring]

PURPOSE

In 2008, the French national society of radiation oncology (SFRO) and the association for radiation oncology continued education (AFCOR) created Siriade, an e-learning website dedicated to contouring.

MATERIAL AND METHODS

Between 2015 and 2017, this platform was updated using the latest digital online tools available. Two main sections were needed: a theoretical part and another section of online workshops.

RESULTS

Teaching courses are available as online commented videos, available on demand. The practical section of the website is an online contouring workshop that automatically generates a report quantifying the quality of the user's delineation compared with the experts'.

CONCLUSION

Siriade 2.0 is an innovating digital tool for radiation oncology initial and continuous education.

Automatic Intracranial Segmentation: Is the Clinician Still Needed?

INTRODUCTION

Stereotactic hypofractionated radiotherapy is an effective treatment for brain metastases in oligometastatic patients. Its planning is however time-consuming because of the number of organs at risk to be manually segmented. This study evaluates 2 automated segmentation commercial software.

METHODS

Patients were scanned in the treatment position. The computed tomography scan was registered on a magnetic resonance imaging and volumes were manually segmented by a clinician. Then 2 automated segmentations were performed (with iPlan and Smart Segmentation). RT STRUCT files were compared with Aquilab's Artistruct segment comparison module. We selected common segmented volume ratio as the main judging criterion. Secondary criteria were Dice-Sørensen coefficients, overlap ratio, and additional segmented volume.

RESULTS

Twenty consecutive patients were included. Agreement between manual and automated contouring was poor. Common segmented volumes ranged from 7.71% to 82.54%, Dice-Sørensen coefficient ranged from 0.0745 to 0.8398, overlap ratio ranged from 0.0414 to 0.7275, and additional segmented volume ranged from 9.80% to 92.25%. Each software outperformed the other on some organs while performing worse on others.

CONCLUSION

No software seemed clearly better than the other. Common segmented volumes were much too low for routine use in stereotactic hypofractionated brain radiotherapy. Manual editing is still needed.

Segmentation editing improves efficiency while reducing inter-expert variation and maintaining accuracy for normal brain tissues in the presence of space-occupying lesions

Image segmentation has become a vital and often rate-limiting step in modern radiotherapy treatment planning. In recent years, the pace and scope of algorithm development, and even introduction into the clinic, have far exceeded evaluative studies. In this work we build upon our previous evaluation of a registration driven segmentation algorithm in the context of 8 expert raters and 20 patients who underwent radiotherapy for large space-occupying tumours in the brain. In this work we tested four hypotheses concerning the impact of manual segmentation editing in a randomized single-blinded study. We tested these hypotheses on the normal structures of the brainstem, optic chiasm, eyes and optic nerves using the Dice similarity coefficient, volume, and signed Euclidean distance error to evaluate the impact of editing on inter-rater variance and accuracy. Accuracy analyses relied on two simulated ground truth estimation methods: simultaneous truth and performance level estimation and a novel implementation of probability maps. The experts were presented with automatic, their own, and their peers' segmentations from our previous study to edit. We found, independent of source, editing reduced inter-rater variance while maintaining or improving accuracy and improving efficiency with at least 60% reduction in contouring time. In areas where raters performed poorly contouring from scratch, editing of the automatic segmentations reduced the prevalence of total anatomical miss from approximately 16% to 8% of the total slices contained within the ground truth estimations. These findings suggest that contour editing could be useful for consensus building such as in developing delineation standards, and that both automated methods and even perhaps less sophisticated atlases could improve efficiency, inter-rater variance, and accuracy.

Development of image-processing software for automatic segmentation of brain tumors in MR images

Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ‘Prometheus,’ which performs neural system–based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively.

[Automatic image segmentation for treatment planning in radiotherapy]

One drawback of the growth in conformal radiotherapy and image-guided radiotherapy is the increased time needed to define the volumes of interest. This also results in inter- and intra-observer variability. However, developments in computing and image processing have enabled these tasks to be partially or totally automated. This article will provide a detailed description of the main principles of image segmentation in radiotherapy, its applications and the most recent results in a clinical context.