Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.
Keyphrases
- deep learning
- coronary artery
- convolutional neural network
- high resolution
- machine learning
- pulmonary artery
- magnetic resonance imaging
- endothelial cells
- ultrasound guided
- healthcare
- primary care
- emergency department
- heart failure
- contrast enhanced ultrasound
- adverse drug
- st elevation myocardial infarction
- photodynamic therapy
- magnetic resonance
- blood flow
- general practice
- fluorescence imaging
- pluripotent stem cells
- atrial fibrillation
- cone beam
- image quality