Lee Yurim, Al Mukaddim Rashid, Ngawang Tenzin, Salamat Shahriar, Mitchell Carol C, Maybock Jenna, Wilbrand Stephanie M, Dempsey Robert J, Varghese Tomy
Medical Physics, University of Wisconsin School of Medicine and Public Health (UW-SMPH), Madison, USA.
Pathology and Laboratory Medicine, UW-SMPH, Madison, USA.
Sci Rep. 2025 Jan 2;15(1):139. doi: 10.1038/s41598-024-83948-6.
Carotid plaques-the buildup of cholesterol, calcium, cellular debris, and fibrous tissues in carotid arteries-can rupture, release microemboli into the cerebral vasculature and cause strokes. The likelihood of a plaque rupturing is thought to be associated with its composition (i.e. lipid, calcium, hemorrhage and inflammatory cell content) and the mechanical properties of the plaque. Automating and digitizing histopathological images of these plaques into tissue specific (lipid and calcified) regions can help us compare histologic findings to in vivo imaging and thereby enable us to optimize medical treatments or interventions for patients based on the composition of plaques. Lack of public datasets and the hypocellular nature of plaques have made applying deep learning to this task difficult. To address this, we sampled 1944 regions of interests from 323 whole slide images and drastically varied their pixel resolution from [Formula: see text] to [Formula: see text] as we anticipated that varying the pixel resolution of histology images can provide neural networks more 'context' that pathologists also rely on. We were able to train Mask R-CNN using regions of interests with varied pixel resolution, with a [Formula: see text] increase in pixel accuracy versus training with patches. The model achieved F1 scores of [Formula: see text] for calcified regions, [Formula: see text] for lipid core with fibrinous material and cholesterol crystals, and [Formula: see text] for fibrous regions, as well as a pixel accuracy of [Formula: see text]. While the F1 score was not calculated for lumen, qualitative results illustrate the model's ability to predict lumen. Hemorrhage was excluded as a class since only one out of 34 carotid endarterectomy specimens had sufficient hemorrhage for annotation.
颈动脉斑块——颈动脉中胆固醇、钙、细胞碎片和纤维组织的堆积——可能会破裂，将微栓子释放到脑血管系统中并导致中风。斑块破裂的可能性被认为与其成分（即脂质、钙、出血和炎症细胞含量）以及斑块的机械性能有关。将这些斑块的组织病理学图像自动数字化为特定组织区域（脂质和钙化区域），可以帮助我们将组织学发现与体内成像进行比较，从而使我们能够根据斑块的成分优化针对患者的医疗治疗或干预措施。缺乏公共数据集以及斑块细胞含量低的特点使得将深度学习应用于这项任务变得困难。为了解决这个问题，我们从323张全切片图像中采样了1944个感兴趣区域，并将它们的像素分辨率从[公式：见原文]大幅变化到[公式：见原文]，因为我们预计改变组织学图像的像素分辨率可以为神经网络提供更多病理学家也依赖的“上下文”信息。我们能够使用具有不同像素分辨率的感兴趣区域训练Mask R-CNN，与使用小块进行训练相比，像素准确率提高了[公式：见原文]。该模型在钙化区域的F1分数为[公式：见原文]，在含有纤维物质和胆固醇晶体的脂质核心区域为[公式：见原文]，在纤维区域为[公式：见原文]，像素准确率为[公式：见原文]。虽然没有计算管腔的F1分数，但定性结果说明了该模型预测管腔的能力。由于34个颈动脉内膜切除术标本中只有1个有足够的出血用于标注，因此将出血排除在类别之外。
