Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade.
Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade.
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DOI：
10.4103/2153-3539.117448
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发表时间：
2013-01-01
影响因子：
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通讯作者：
Wang, May D
中科院分区：
文献类型：
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作者：
Kothari, Sonal;Phan, John H;Wang, May D
背景：组织活检全切片图像（WSIs）的分析依赖于有效检测和消除图像伪影。我们提出了一种新的方法来检测组织病理学WSI中的组织折叠伪影。我们还研究了组织褶皱对图像特征和预测models.MATERIALS和METHODS的影响：我们使用来自两个癌症终点-肾透明细胞癌（KiCa）和卵巢浆液性腺癌（OvCa）-公开可从癌症基因组图谱的样品的WSI。我们使用颜色属性和两个自适应基于连通性的阈值检测低分辨率WSI中的组织褶皱。我们使用来自两个癌症终点的105个手动注释的WSI优化和验证我们的组织折叠检测方法。除了检测组织褶皱外，我们还从所有样本的高分辨率WSI中提取了461个图像特征。我们使用的秩和检验，以找到图像的功能，统计学上不同的功能，从同一组的WSIs和没有折叠。然后，我们使用受组织褶皱影响的特征来开发用于预测癌症等级的模型。结果：当与地面真实值相比时，我们的方法检测KiCa中的组织褶皱，具有0.50的调整兰德指数（ARI），0.77的平均真实率（ATR），0.55的真实阳性率（TPR）和0.98的真实阴性率（TNR）; OvCa组ARI为0.40，ATR为0.73，TPR为0.47，TNR为0.98。与其他两种方法相比，我们的方法是更准确的ARI和ATR。我们发现，53和30个图像特征分别受到OvCa和KiCa中存在的组织折叠伪影（使用我们的方法检测到）的显著影响。消除组织褶皱后，OvCa和KiCa的癌症等级预测模型的性能分别提高了5%和1%.CONCLUSION：与其他方法相比，所提出的基于连接性的方法在检测组织褶皱方面更有效。减少组织折叠伪影将提高癌症分级预测模型的性能。
BACKGROUND: Analysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models.MATERIALS AND METHODS: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades.RESULTS: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively.CONCLUSION: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.