论文标题
通过签名距离图的深层回归,镜面显微镜图像中的角膜内皮评估
Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps
论文作者
论文摘要
由于存在称为guttae的黑图像区域的存在,富氏营养不良的人角膜内皮(CE)的镜面显微镜评估具有挑战性。本文提出了一种基于UNET的分割方法,需要在所有程度的Fuchs营养不良症中,需要最少的后处理,并实现可靠的CE形态计量评估和GUTTAE鉴定。我们将分割问题作为单元格的回归任务施加,而GUTTA签名的距离图,而不是像UNET所做的那样,而不是像素级分类任务。与常规的UNET分类方法相比,距离映射回归方法在临床相关参数中的收敛速度更快。它还产生形态计量参数,该参数与手动分段的基接地数据一致,即平均细胞密度差为-41.9单元/mm2(95%置信区间(CI)[-306.2,222.5])和平均单元格面积的平均平均差异为14.8 um2(95%CI [-41.9,71.9,71.9,71.9,71.9,71.9,71.5])。这些结果表明了CE评估的有希望的替代方法。
Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs' dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.