A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images.
Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 5; pp. 2186 - 2195 |
|---|---|
| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2024
|
| 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=181515387&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181515387 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2024 vid: 37 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 181515387 181515387 181515387 10.1007/s10278-024-01072-3 181515387 ppf: 2186 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images. aug: au: Wang, Peilong Kline, Timothy L. Missert, Andrew D. Cook, Cole J. Callstrom, Matthew R. Chan, Alex Hartman, Robert P. Kelm, Zachary S. Korfiatis, Panagiotis affil: https://ror.org/02qp3tb03 Department of Radiology, Mayo Clinic, Rochester, MN, USA sug: subj: Liver Pathology Cysts Radiography Workflow Colorectal Neoplasms Neoplasm Metastasis Radiography Image Processing, Computer Assisted Deep Learning Utilization Liver Neoplasms Radiography Liver Neoplasms Classification Human Tomography, X-Ray Computed Wilcoxon Signed Rank Test Pilot Studies Descriptive Statistics Confidence Intervals Liver Radiography Cancer Patients ab: Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility of building a workflow to efficiently route images to specifically trained segmentation models. By implementing a deep learning classifier to automatically classify the images and route them to appropriate segmentation models, we hope that our workflow can segment the images with different pathology accurately. The data we used in this study are 350 CT images from patients affected by polycystic liver disease and 350 CT images from patients presenting with liver metastases from colorectal cancer. All images had the liver manually segmented by trained imaging analysts. Our proposed adaptive segmentation workflow achieved a statistically significant improvement for the task of total liver segmentation compared to the generic single-segmentation model (non-parametric Wilcoxon signed rank test, n = 100, p-value << 0.001). This approach is applicable in a wide range of scenarios and should prove useful in clinical implementations of segmentation pipelines. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|