Weakly-and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts  

作　　者：Alex Ling Yu Hung Edward Chen John Galeotti 

机构地区：[1]Department of Biomedical Engineering,Carnegie Mellon University,Pittsburgh,PA,USA [2]Robotics Institute,Carnegie Mellon University,Pittsburgh,PA,USA

出　　处：《Biomedical Engineering Frontiers》2022年第1期67-81,共15页生物医学工程前沿（英文）

基　　金：sponsored in part by the US Army Medical contracts W81XWH-19-C0083,W81XWH-19-C0101,and W81XWH-19-C-0020;funding from the state of Pennsylvania C000072473.

摘　　要：Objective and Impact Statement.We propose a weakly-and semisupervised,probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts.Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error.Introduction.Ultrasound image quality has continually been improving.However,when needles or other metallic objects are operating inside the tissue,the resulting reverberation artifacts can severely corrupt the surrounding image quality.Such effects are challenging for existing computer vision algorithms for medical image analysis.Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees.The boundaries of such artifacts are ambiguous,leading to disagreement among human experts labeling the artifacts.Methods.Our learning-based framework consists of three parts.The first part is a probabilistic segmentation network to generate the soft labels based on the human labels.These soft labels are input into the second part which is the transform function,where the training labels for the third part are generated.The third part outputs the final masks which quantifies the reverberation artifacts.Results.We demonstrate the applicability of the approach and compare it against other segmentation algorithms.Our method is capable of both differentiating between the reverberations from artifact-free patches and modeling the intensity fall-off in the artifacts.Conclusion.Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy,especially in the immediately adjacent regions between reverberation lines.Our algorithm is also able to improve the performance of downstream image analysis algorithms.

关 键 词：image PROBABILISTIC ANATOMY 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] R445.1[自动化与计算机技术—计算机科学与技术]