3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation

Abstract

Objective: Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences. Approach: In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction. Main results: Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%. Significance: The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.

Is denoising necessary for ultrasound image segmentation deep learning: review and benchmark

“…SABR-Net [103] addressed the missing and ambiguous boundaries in the contexts of shadow artifacts via semi-supervised shadow-aware network with boundary refinement, by adding shadow imitation regions to the original images and design shadow-masked tranformer blocks to perceive missing anatomy. A densely connected 3D pyramidal dilated convolutuion network [104] is proposed with sequential cross-frame uncertainty guidance to exploit the longitudinal information and perceive size-varied vessel regions for intravascular ultrasound sequence segmentation. All of these boundary correction schemes can explore the transcending boundary edge contextual information to filter out the unreliable boundary or edge predictions in intermediate feature maps and multi-scale consistency segmentation.…”