Reed Karen L, Conzemius Michael G, Robinson Robert A, Brown Thomas D
Department of Orthopaedics and Rehabilitation, University of Iowa, IA 52242, USA.
Comput Methods Biomech Biomed Engin. 2004 Feb;7(1):25-32. doi: 10.1080/10255840310001634250.
Femoral head osteonecrosis is often characterized histologically by the presence of empty lacunae in the affected bony regions. The shape, size and location of a necrotic lesion influences prognosis, and can, in principle, be quantified by mapping the distribution of empty lacunae within a femoral head. An algorithm is here described that automatically identifies the locations of osteocyte-filled vs. empty lacunae. The algorithm is applied to necrotic lesions surgically induced in the emu, a large bipedal animal model in which osteonecrosis progresses to collapse, as occurs in humans. The animals' femoral heads were harvested at sacrifice, and hematoxylin and eosin-stained histological preparations of the coronal midsections were digitized and image-analyzed. The algorithm's performance in detecting empty lacunae was validated by comparing its results to corresponding assessments by six trained histologists. The percentage of osteocyte-filled lacunae identified by the algorithm vs. by the human readers was statistically indistinguishable.
股骨头坏死在组织学上通常表现为受累骨区域存在空骨陷窝。坏死灶的形状、大小和位置会影响预后，原则上可通过绘制股骨头内空骨陷窝的分布来进行量化。本文描述了一种算法，可自动识别充满骨细胞的骨陷窝与空骨陷窝的位置。该算法应用于鸸鹋（一种大型双足动物模型，其股骨头坏死会发展为塌陷，类似于人类情况）手术诱导的坏死灶。在处死动物时采集其股骨头，并将冠状中截面苏木精和伊红染色的组织学切片数字化并进行图像分析。通过将算法结果与六位训练有素的组织病理学家的相应评估结果进行比较，验证了该算法在检测空骨陷窝方面的性能。算法识别出的充满骨细胞的骨陷窝百分比与人类读者识别出的百分比在统计学上无显著差异。
