| Title: |
Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA |
| Authors: |
van Praagh, Gijs D; Nienhuis,Pieter H; Reijrink,Melanie; Davidse,Mirjam E J; Duff,Lisa M; Spottiswoode,Bruce S; Mulder,Douwe J; Prakken,Niek H J; Scarsbrook,Andy F; Morgan,Ann W; Tsoumpas,Charalampos; Wolterink,Jelmer M; Mouridsen,Kim B; Borra,Ronald J H; Sinha,Bhanu; Slart,Riemer H J A |
| Publication Year: |
2024 |
| Subject Terms: |
Aorta/diagnostic imaging; Aortic Diseases/diagnostic imaging; Automation; Feasibility Studies; Female; Humans; Image Processing; Computer-Assisted/methods; Male; Positron Emission Tomography Computed Tomography; Journal Article |
| Description: |
BACKGROUND: Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. PURPOSE: To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. METHODS: A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. RESULTS: Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT ... |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| ISSN: |
0094-2405 |
| Relation: |
https://dspace.library.uu.nl/handle/1874/465975 |
| Availability: |
https://dspace.library.uu.nl/handle/1874/465975 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.B12D3037 |
| Database: |
BASE |