Discussion
From this, several conclusions can be derived. Important studies in stiffness assessment reported ambiguity caused by the non-uniformity in external distance measurement. Further, the external distance measurement does not consider the internal contour of the artery which is important. Researchers presented a difference in reference values of PWV owing to local or regional measurement. The image-based techniques propose an added advantage of presenting a clear idea of atherosclerosis. These techniques are either fully automated or semi, based on supervisor involvement. Moreover, these methods required arteries to be straight and horizontal for best performance. Further, noise and artefacts deteriorated the assessment. Evidence presented here is conclusive on the success of DL in lumen characterisation and segmentation. This paper proposes deep learning-based stiffness measurement from cine-loop, a short (ultrasound) video, of the carotid artery. Cine-loop capture IMT and LD variations over many cardiac cycles and averaging over multiple frames diminish the effects of noise. Recently, Patel et al. [137] estimated elasticity using ELM. ELM was applied for ROI localisation, and in 100 images maximum-IMT and maximum-LD error were 20 mm and 91 mm, respectively. However, the study estimated Young’s modulus and at present little is known about the correlation between modulus of elasticity and cfPWV in a clinical setup.
The proposed method includes four stages of operation (illustration in Figure 4.): pre-processing, ROI localisation: extraction of lumen region, LD/IMT assessment and stiffness computation. The purpose of pre-processing is auto-crop of textual data with emphasis on tissue region. Pre-processing involves tasks such as brightness transformation, geometric transformation edge detection and image restoration in preparing the ultrasound image for the training and testing. Manual tracing by experts is used as GT for training. Networks for lumen and/or intima-adventitia are trained using separate GT. Further, down sampling is applied to improve processing efficiency. Often ECG recordings are made along with the carotid scan. R-wave is used to synchronize frames in consecutive cardiac cycles. IMT is processed from frames 5 seconds after R-wave to capture the extreme values during the two phases of the cardiac cycle: systole and diastole. Lumen region and IMC region are the ROI in stiffness computation leading to stiffness computation as analysed in Section 2 of this paper. ROI localisation is applied twice in the entire process: one for the identification of LI-far region and two for recognition of LI-near zone. LI-far facilitates the computation of IMT. Both LI-far and LI-near were required for the assessment of LD. LD/IMT assessment was performed using a poly-line distance method.