Ultrasound Confidence Maps Of Intensity And Structure Based On Directed Acyclic Graphs And Artifact Models

Hung, Alex Ling Yu; Chen, Wanwen; Galeotti, John

doi:10.1109/isbi48211.2021.9433862

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…However, the method fails to optimize the out-plane orientation, which is controlled using the teleportation of a robotic arm. Jiang et al [21] presented a method for optimizing the in-plane orientation of robotic ultrasound probe based on confidence map-based [22], [23] assessment of ultrasound image quality. For outplane orientation, they further used the force measurements to align central axis of probe with the normal to anatomical surface.…”

Section: A Related Work On Robotized Probe Normalizationmentioning

confidence: 99%

Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation

Raina

Verma

Chandrashekhara

et al. 2023

Preprint

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We investigate the applicability of U-Net based models for segmenting Urinary Bladder (UB) in male pelvic view UltraSound (US) images. The segmentation of UB in the US image aids radiologists in diagnosing the UB. However, UB in US images has arbitrary shapes, indistinct boundaries and considerably large inter- and intra-subject variability, making segmentation a quite challenging task. Our study of the state-of-the-art (SOTA) segmentation network, U-Net, for the problem reveals that it often fails to capture the salient characteristics of UB due to the varying shape and scales of anatomy in the noisy US image. Also, U-net has an excessive number of trainable parameters, reporting poor computational efficiency during training. We propose a Slim U-Net to address the challenges of UB segmentation. Slim U-Net proposes to efficiently preserve the salient features of UB by reshaping the structure of U-Net using a less number of 2D convolution layers in the contracting path, in order to preserve and impose them on expanding path. To effectively distinguish the blurred boundaries, we propose a novel annotation methodology, which includes the background area of the image at the boundary of a marked region of interest (RoI), thereby steering the model's attention towards boundaries. In addition, we suggested a combination of loss functions for network training in the complex segmentation of UB. The experimental results demonstrate that Slim U-net is statistically superior to U-net for UB segmentation. The Slim U-net further decreases the number of trainable parameters and training time by 54% and 57.7%, respectively, compared to the standard U-Net, without compromising the segmentation accuracy.

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Section: A Related Work On Robotized Probe Normalizationmentioning

confidence: 99%

Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation

Raina

Verma

Chandrashekhara

et al. 2023

Preprint

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Ultrasound Confidence Maps with Neural Implicit Representation

Yesilkaynak,

Duque,

Wysocki

et al. 2024

Medical Image Understanding and Analysis

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Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

Hung

Galeotti

2021

Int J CARS

Self Cite

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Purpose Ultrasound compounding is to combine sonographic information captured from different angles and produce a single image. It is important for multi-view reconstruction, but as of yet there is no consensus on best practices for compounding. Current popular methods inevitably suppress or altogether leave out bright or dark regions that are useful and potentially introduce new artifacts. In this work, we establish a new algorithm to compound the overlapping pixels from different viewpoints in ultrasound. Methods Inspired by image fusion algorithms and ultrasound confidence, we uniquely leverage Laplacian and Gaussian pyramids to preserve the maximum boundary contrast without overemphasizing noise, speckles, and other artifacts in the compounded image, while taking the direction of the ultrasound probe into account. Besides, we designed an algorithm that detects the useful boundaries in ultrasound images to further improve the boundary contrast. Results We evaluate our algorithm by comparing it with previous algorithms both qualitatively and quantitatively, and we show that our approach not only preserves both light and dark details, but also somewhat suppresses noise and artifacts, rather than amplifying them. We also show that our algorithm can improve the performance of downstream tasks like segmentation. Conclusion Our proposed method that is based on confidence, contrast, and both Gaussian and Laplacian pyramids appears to be better at preserving contrast at anatomic boundaries while suppressing artifacts than any of the other approaches we tested. This algorithm may have future utility with downstream tasks such as 3D ultrasound volume reconstruction and segmentation.

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Ultrasound Confidence Maps Of Intensity And Structure Based On Directed Acyclic Graphs And Artifact Models

Cited by 4 publications

References 15 publications

Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation

Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation

Ultrasound Confidence Maps with Neural Implicit Representation

Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

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