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首页> 外文会议>International Workshop on Prostate Cancer Imaging >High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies
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High-Throughput Prostate Cancer Gland Detection, Segmentation, and Classification from Digitized Needle Core Biopsies

机译：从数字化针心活组织检查的高通量前列腺癌症腺体检测，分割和分类

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We present a high-throughput computer-aided system for the segmentation and classification of glands in high resolution digitized images of needle core biopsy samples of the prostate. It will allow for rapid and accurate identification of suspicious regions on these samples. The system includes the following three modules: 1) a hierarchical frequency weighted mean shift normalized cut (HNCut) for initial detection of glands; 2) a geodesic active contour (GAC) model for gland segmentation; and 3) a diffeomorphic based similarity (DBS) feature extraction for classification of glands as benign or cancerous. HNCut is a minimally supervised color based detection scheme that combines the frequency weighted mean shift and normalized cuts algorithms to detect the lumen region of candidate glands. A GAC model, initialized using the results of HNCut, uses a color gradient based edge detection function for accurate gland segmentation. Lastly, DBS features are a set of morpho-metric features derived from the nonlinear dimensionality reduction of a dissimilarity metric between shape models. The system integrates these modules to enable the rapid detection, segmentation, and classification of glands on prostate biopsy images. Across 23 H & E stained prostate studies of whole-slides, 105 regions of interests (ROIs) were selected for the evaluation of segmentation and classification. The segmentation results were evaluated on 10 ROIs and compared to manual segmentation in terms of mean distance (2.6 ± 0.2 pixels), overlap (62 ± 0.07%), sensitivity (85±0.01%), specificity (94±0.003%) and positive predictive value (68 ± 0,08%). Over 105 ROIs, the classification accuracy for glands automatically segmented was (82.5 ±9.10%) while the accuracy for glands manually segmented was (82.89 ±3.97%); no statistically significant differences were identified between the classification results.
机译：我们为高分辨率数字化图像中的针芯活检样品的高分辨率数字化图像进行了高吞吐量的计算机辅助系统，用于分割和分类。它将允许快速准确地识别这些样本上的可疑区域。该系统包括以下三个模块：1）分层频率加权平均移位归一化切割（HNCUT），用于初始检测腺体; 2）Gland分段的测地有源轮廓（GAC）模型; 3）基于群体的相似性（DBS）特征提取，用于分类腺体，良性或癌症。 HNCUT是一种微量监督的基于颜色的检测方案，其将频率加权平均移位和标准化切割算法组合以检测候选腺体的内腔区域。使用HNCUT的结果初始化GAC模型，使用基于颜色的梯度基于的边缘检测功能进行准确的腺体分段。最后，DBS特征是一组Morpho-urc标准特征，导致了形状模型之间的非线性度量的非线性维度降低。该系统集成了这些模块，以实现前列腺活组织检查图像上的腺体的快速检测，分割和分类。在23个H＆E染色的全幻灯片前列腺研究，选择了105个兴趣区域（ROI）进行分割和分类评估。分段结果在10个ROIS上进行评估，与平均距离（2.6±0.2像素），重叠（62±0.07％），灵敏度（85±0.01％），特异性（94±0.003％）和阳性的手动分段进行比较。预测值（68±0,08％）。超过105次ROI，自动细分的腺体的分类精度是（82.5±9.10％），而手动细分的腺体的准确性是（82.89±3.97％）;在分类结果之间没有确定统计学上的显着差异。

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