POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation.
POSEA: A novel algorithm to evaluate the performance of multi-object instance image segmentation.
复制标题
DOI：
10.1371/journal.pone.0283692
复制
发表时间：
2023
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
已经开发了许多技术和软件包来分割显微镜图像内的单个细胞，需要一种稳健的方法来评估分割成大量独特对象的图像。目前，分割图像通常在像素级与地面实况图像进行比较;然而，这种标准的像素级方法无法计算由于像素不正确地分配给相邻对象而导致的误差。在这里，我们定义了一个每对象分割评估算法（POSEA），计算每个分割对象相对于地面实况分割图像的分割精度指标。为了证明POSEA的性能，精确度，召回率和f-测量指标计算和比较与标准像素级评估模拟图像和分段荧光显微镜图像的三个不同的细胞样本。POSEA产生较低的准确性指标比标准的像素级评估，由于正确的会计相邻对象的误分类像素。因此，POSEA为像素被错误分配给相邻对象的对象提供了准确的评估指标，并且对于需要评估独特相邻对象分割的各种应用程序来说都是鲁棒的。
Many techniques and software packages have been developed to segment individual cells within microscopy images, necessitating a robust method to evaluate images segmented into a large number of unique objects. Currently, segmented images are often compared with ground-truth images at a pixel level; however, this standard pixel-level approach fails to compute errors due to pixels incorrectly assigned to adjacent objects. Here, we define a per-object segmentation evaluation algorithm (POSEA) that calculates segmentation accuracy metrics for each segmented object relative to a ground truth segmented image. To demonstrate the performance of POSEA, precision, recall, and f-measure metrics are computed and compared with the standard pixel-level evaluation for simulated images and segmented fluorescence microscopy images of three different cell samples. POSEA yields lower accuracy metrics than the standard pixel-level evaluation due to correct accounting of misclassified pixels of adjacent objects. Therefore, POSEA provides accurate evaluation metrics for objects with pixels incorrectly assigned to adjacent objects and is robust for use across a variety of applications that require evaluation of the segmentation of unique adjacent objects.
登录
查看更多内容
DOI：
10.1083/jcb.115.4.887
发表时间：
1991-11
期刊：
The Journal of cell biology
影响因子：
--
作者：
Kozak M
通讯作者：
Kozak M
影响因子：
12.8
作者：
Cha J;Lee I
通讯作者：
Lee I
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
3.8
作者：
Gieselmann, V
通讯作者：
Gieselmann, V
自发荧光寿命图像数据中稀有细胞群的识别。
DOI：
10.1002/cyto.a.24534
发表时间：
2022-06
期刊：
Cytometry. Part A : the journal of the International Society for Analytical Cytology
影响因子：
--
作者：
通讯作者：
--