-
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
It remains challenging to automatically segment kidneys in clinical ultr...
read it
-
Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active co...
read it
-
Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images
Boundary incompleteness raises great challenges to automatic prostate se...
read it
-
Subsequent Boundary Distance Regression and Pixelwise Classification Networks for Automatic Kidney Segmentation in Ultrasound Images
It remains challenging to automatically segment kidneys in clinical ultr...
read it
-
Good and Bad Boundaries in Ultrasound Compounding: Preserving Anatomic Boundaries While Suppressing Artifacts
Ultrasound 3D compounding is important for volumetric reconstruction, bu...
read it
-
Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
Image segmentation is a fundamental vision task and a crucial step for m...
read it
-
Power-SLIC: Diagram-based superpixel generation
Superpixel algorithms, which group pixels similar in color and other low...
read it
Contrastive Rendering for Ultrasound Image Segmentation
Ultrasound (US) image segmentation embraced its significant improvement in deep learning era. However, the lack of sharp boundaries in US images still remains an inherent challenge for segmentation. Previous methods often resort to global context, multi-scale cues or auxiliary guidance to estimate the boundaries. It is hard for these methods to approach pixel-level learning for fine-grained boundary generating. In this paper, we propose a novel and effective framework to improve boundary estimation in US images. Our work has three highlights. First, we propose to formulate the boundary estimation as a rendering task, which can recognize ambiguous points (pixels/voxels) and calibrate the boundary prediction via enriched feature representation learning. Second, we introduce point-wise contrastive learning to enhance the similarity of points from the same class and contrastively decrease the similarity of points from different classes. Boundary ambiguities are therefore further addressed. Third, both rendering and contrastive learning tasks contribute to consistent improvement while reducing network parameters. As a proof-of-concept, we performed validation experiments on a challenging dataset of 86 ovarian US volumes. Results show that our proposed method outperforms state-of-the-art methods and has the potential to be used in clinical practice.
READ FULL TEXT
Comments
There are no comments yet.