Details

Autor(en) / Beteiligte

• Rajpoot, Kashif
• Grau, Vicente
• Alison Noble, J.
• Becher, Harald
• Szmigielski, Cezary

Titel

The evaluation of single-view and multi-view fusion 3D echocardiography using image-driven segmentation and tracking

Ist Teil von

• Medical image analysis, 2011-08, Vol.15 (4), p.514-528

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2011

Quelle

MEDLINE

Beschreibungen/Notizen

• Arrows indicate LV segmentation failure on single view images. Note the success on multi-view images. LV segmentation failure is defined as the inability of the image-driven segmentation method to reach the endocardial boundary. [Display omitted] ► RT3DE suffer from poor quality due to attenuation and intensity drop-out. ► Multi-view fusion RT3DE improves anatomical information and image quality. ► Image-driven segmentation and tracking developed to fully exploit image information. ► Evaluation of single-view and multi-view RT3DE using segmentation and tracking. ► Multi-view fusion offers better capabilities for image analysis. Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images.