本文選題：前列腺分割 + CT計劃圖像��； 參考：《應用科學學報》2017年01期

【摘要】：為了實現(xiàn)CT計劃圖像中前列腺的自動分割,提出一種基于群體CT計劃圖像的多任務前列腺分割方法.將群體CT計劃圖像分別映射到不同參考圖像空間,形成多個訓練任務.利用隨機森林算法和自動上下文模型訓練出一系列隨機森林分類器,將分類器作用在待分割CT計劃圖像上獲得多個分類概率圖,最后使用多數(shù)投票法求得最終分割結(jié)果.實驗表明,與單任務分割方法相比,基于群體CT圖像的多任務分割能有效提高CT計劃圖像中前列腺的分割準確率.
[Abstract]:In order to realize automatic prostate segmentation in CT planning image, a multitask prostate segmentation method based on colony CT planning image is proposed. The group CT planning images are mapped to different reference image spaces to form multiple training tasks. A series of random forest classifiers are trained by using the stochastic forest algorithm and the automatic context model. The classifier is used to obtain multiple classification probability maps on the CT planned images to be segmented. Finally, the final segmentation results are obtained by majority voting method. Experimental results show that multitask segmentation based on colony CT image can effectively improve the accuracy of prostate segmentation in planned CT images compared with single-task segmentation method.
【作者單位】： 南京郵電大學地理與生物信息學院;南京郵電大學通信與信息工程學院;
【基金】：國家自然科學基金(No.31671006)資助
【分類號】：R737.25;TP391.41
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本文編號：1776436