Segmentation and Measurement of Chronic Wounds for Bioprinting
Segmentation and Measurement of Chronic Wounds for Bioprinting
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DOI：
10.1109/jbhi.2017.2743526
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发表时间：
2018-07-01
影响因子：
7.7
通讯作者：
Kayvanrad, Mohammad
中科院分区：
文献类型：
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作者：
Gholami, Peyman;Ahmadi-pajouh, Mohammad Ali;Kayvanrad, Mohammad
目的：提供一种概念验证工具，用于对慢性伤口进行分割，并将结果作为指令和坐标传输给生物打印机器人，从而促进慢性伤口的治疗。方法：对15例受试者的26幅图像进行边缘检测和形态学操作、区域生长、Livewire、活动轮廓和纹理分割等几种伤口几何形状分割方法的比较。真实的伤口描绘是由皮肤科医生生成的。伤口坐标被转换成生物打印机器人可以理解的g代码。采用16% （w/v）海藻酸钠和4% （w/v）明胶在去离子水中溶解的方法合成了海藻酸盐水凝胶，用于细胞包封。结果：Livewire的准确性、灵敏度、特异度、Jaccard指数、Dice相似系数和Hausdorff距离的平均值分别为97.08%、99.68%、96.67%、96.22、98.15和32.26，用户交互最小，表现最佳。生物打印机机器人能够在皮肤表面打印皮肤细胞，生物打印贴片的尺寸与所需伤口几何形状之间的相似性为95.56%。结论：我们设计了一种基于伤口图像半自动分割的慢性伤口愈合新方法，通过更精确的坐标来提高临床医生对生物打印过程的控制。意义：本研究首次实现了基于图像分割的伤口生物打印。它还比较了用于此目的的几种分割方法，以确定最佳分割方法。
Objective: to provide a proof-of-concept tool for segmenting chronic wounds and transmitting the results as instructions and coordinates to a bioprinter robot and thus facilitate the treatment of chronic wounds. Methods: several segmentation methods used for measuring wound geometry, including edge-detection and morphological operations, region-growing, Livewire, active contours, and texture segmentation, were compared on 26 images from 15 subjects. Ground-truth wound delineations were generated by a dermatologist. The wound coordinates were converted into G-code understandable by the bioprinting robot. Due to its desirable properties, alginate hydrogel was synthesized by dissolving 16% (w/v) sodium-alginate and 4% (w/v) gelatin in deionized water and used for cell encapsulation. Results: Livewire achieved the best performance, with minimal user interaction: 97.08%, 99.68% 96.67%, 96.22, 98.15, and 32.26, mean values, respectively, for accuracy, sensitivity, specificity, Jaccard index, Dice similarity coefficient, and Hausdorff distance. The bioprinter robot was able to print skin cells on the surface of skin with a 95.56% similarity between the bioprinted patch's dimensions and the desired wound geometry. Conclusion: we have designed a novel approach for the healing of chronic wounds, based on semiautomatic segmentation of wound images, improving clinicians' control of the bioprinting process through more accurate coordinates. Significance: this study is the first to perform wound bioprinting based on image segmentation. It also compares several segmentation methods used for this purpose to determine the best.