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首页> 外国专利> DETECTION OF PROSTATE CANCER IN MULTI-PARAMETRIC MRI USING RANDOM FOREST

DETECTION OF PROSTATE CANCER IN MULTI-PARAMETRIC MRI USING RANDOM FOREST

机译：随机林检测多参数MRI中的前列腺癌

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Disclosed prostate computer aided diagnosis (CAD) systems employ a Random Forest classifier to detect prostate cancer. System classify individual pixels inside the prostate as potential sites of cancer using a combination of spatial, intensity and texture features extracted from three sequences. The Random Forest training considers instance-level weighting for equal treatment of small and large cancerous lesions and small and large prostate backgrounds. Two other approaches are based on an AutoContext pipeline intended to make better use of sequence-specific patterns. Also disclosed are methods and systems for accurate automatic segmentation of the prostate in MRI. Methods can include both patch-based and holistic (image-to-image) deep learning methods for segmentation of the prostate. A patch-based convolutional network aims to refine the prostate contour given an initialization. A method for end-to-end prostate segmentation integrates holistically nested edge detection with fully convolutional networks. HNNs automatically learn a hierarchical representation that improve prostate boundary detection.
机译：公开了前列腺计算机辅助诊断（CAD）系统使用随机林分类器来检测前列腺癌。系统在三个序列中提取的空间，强度和纹理特征的组合将前列腺内的各个像素分类为癌症的潜在站点。随机森林培训考虑了平等治疗小型和大型癌变病变和小型前列腺背景的案例级别。另外两种方法基于自动文本管道，旨在更好地利用序列特定的模式。还公开了用于在MRI中准确自动分段的方法和系统。方法可以包括用于分割前列腺的贴片和整体（图像到图像）深度学习方法。基于补丁的卷积网络旨在优化给定初始化前列腺轮廓。端到端前列腺分段的方法与完全卷积网络集成了全面嵌套边缘检测。 HNNS自动学习改进前列腺边界检测的分层表示。

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