Berthon Beatrice, Marshall Christopher, Evans Mererid, Spezi Emiliano
Wales Research & Diagnostic PET Imaging Centre, Cardiff University, CF14 4XN, Cardiff, UK.
Phys Med Biol. 2016 Jul 7;61(13):4855-69. doi: 10.1088/0031-9155/61/13/4855. Epub 2016 Jun 8.
Accurate and reliable tumour delineation on positron emission tomography (PET) is crucial for radiotherapy treatment planning. PET automatic segmentation (PET-AS) eliminates intra- and interobserver variability, but there is currently no consensus on the optimal method to use, as different algorithms appear to perform better for different types of tumours. This work aimed to develop a predictive segmentation model, trained to automatically select and apply the best PET-AS method, according to the tumour characteristics. ATLAAS, the automatic decision tree-based learning algorithm for advanced segmentation is based on supervised machine learning using decision trees. The model includes nine PET-AS methods and was trained on a 100 PET scans with known true contour. A decision tree was built for each PET-AS algorithm to predict its accuracy, quantified using the Dice similarity coefficient (DSC), according to the tumour volume, tumour peak to background SUV ratio and a regional texture metric. The performance of ATLAAS was evaluated for 85 PET scans obtained from fillable and printed subresolution sandwich phantoms. ATLAAS showed excellent accuracy across a wide range of phantom data and predicted the best or near-best segmentation algorithm in 93% of cases. ATLAAS outperformed all single PET-AS methods on fillable phantom data with a DSC of 0.881, while the DSC for H&N phantom data was 0.819. DSCs higher than 0.650 were achieved in all cases. ATLAAS is an advanced automatic image segmentation algorithm based on decision tree predictive modelling, which can be trained on images with known true contour, to predict the best PET-AS method when the true contour is unknown. ATLAAS provides robust and accurate image segmentation with potential applications to radiation oncology.
在正电子发射断层扫描（PET）上进行准确可靠的肿瘤轮廓描绘对于放射治疗计划至关重要。PET自动分割（PET-AS）消除了观察者内部和观察者之间的变异性，但目前对于使用的最佳方法尚无共识，因为不同的算法似乎对不同类型的肿瘤表现更好。这项工作旨在开发一种预测分割模型，该模型经过训练可根据肿瘤特征自动选择并应用最佳的PET-AS方法。ATLAAS，即基于决策树的高级分割自动学习算法，是基于使用决策树的监督机器学习。该模型包括九种PET-AS方法，并在100次具有已知真实轮廓的PET扫描上进行了训练。为每种PET-AS算法构建了一个决策树，以根据肿瘤体积、肿瘤峰值与背景SUV比值以及区域纹理度量来预测其准确性，使用骰子相似系数（DSC）进行量化。对从可填充和打印的亚分辨率夹心体模获得的85次PET扫描评估了ATLAAS的性能。ATLAAS在广泛的体模数据中显示出优异的准确性，在93%的病例中预测出最佳或接近最佳的分割算法。在可填充体模数据上，ATLAAS的表现优于所有单一的PET-AS方法，DSC为0.881，而头颈部体模数据的DSC为0.819。在所有情况下DSC均高于0.650。ATLAAS是一种基于决策树预测建模的先进自动图像分割算法，可在具有已知真实轮廓的图像上进行训练，以便在真实轮廓未知时预测最佳的PET-AS方法。ATLAAS提供了强大而准确的图像分割，在放射肿瘤学中具有潜在应用。
