What Matters in Radiological Image Segmentation? Effect of Segmentation Errors on the Diagnostic Related Features.
Segmentation is a crucial step in extracting the medical image features for clinical diagnosis. Though multiple metrics have been proposed to evaluate the segmentation performance, there is no clear study on how or to what extent the segmentation errors will affect the diagnostic related features us...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2088 - 2100 |
|---|---|
| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950870&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950870 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950870 164410947 171950870 171950870 10.1007/s10278-023-00865-2 171950870 ppf: 2088 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: What Matters in Radiological Image Segmentation? Effect of Segmentation Errors on the Diagnostic Related Features. aug: au: Chen, Zihang Chen, Jiafei Zhao, Jun Liu, Bowei Jiang, Shuanglong Si, Dongyue Ding, Haiyan Nian, Yongjian Yang, Xiaochao Xiao, Jingjing affil: https://ror.org/023rhb549 Bioengineering College, Chongqing University, Chongqing, China sug: subj: Image Processing, Computer Assisted User-Computer Interface Computer Graphics Diagnostic Imaging Evaluation Human Experimental Studies Algorithms Deep Learning Funding Source Magnetic Resonance Imaging Methods Radiology Information Systems Time Series T-Tests Descriptive Statistics ab: Segmentation is a crucial step in extracting the medical image features for clinical diagnosis. Though multiple metrics have been proposed to evaluate the segmentation performance, there is no clear study on how or to what extent the segmentation errors will affect the diagnostic related features used in clinical practice. Therefore, we proposed a segmentation robustness plot (SRP) to build the link between segmentation errors and clinical acceptance, where relative area under the curve (R-AUC) was designed to help clinicians to identify the robust diagnostic related image features. In experiments, we first selected representative radiological series from time series (cardiac first-pass perfusion) and spatial series (T2 weighted images on brain tumors) of magnetic resonance images, respectively. Then, dice similarity coefficient (DSC) and Hausdorff distance (HD), as the widely used evaluation metrics, were used to systematically control the degree of the segmentation errors. Finally, the differences between diagnostic related image features extracted from the ground truth and the derived segmentation were analyzed, using the statistical method large sample size T-test to calculate the corresponding p values. The results are denoted in the SRP, where the x-axis indicates the segmentation performance using the aforementioned evaluation metric, and the y-axis shows the severity of the corresponding feature changes, which are expressed in either the p values for a single case or the proportion of patients without significant change. The experimental results in SRP show that when DSC is above 0.95 and HD is below 3 mm, the segmentation errors will not change the features significantly in most cases. However, when segmentation gets worse, additional metrics are required for further analysis. In this way, the proposed SRP indicates the impact of the segmentation errors on the severity of the corresponding feature changes. By using SRP, one could easily define the acceptable segmentation errors in a challenge. Additionally, the R-AUC calculated from SRP provides an objective reference to help the selection of reliable features in image analysis. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|