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

Sierra, Juan S., Pineda, Jesus, Rueda, Daniela, Tello, Alejandro, Prada, Angelica M., Galvis, Virgilio, Volpe, Giovanni, Millan, Maria S., Romero, Lenny A., Marrugo, Andres G.

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源