![]() I'm confident that Chris' skills and knowledge will prove invaluable to our organization as we drive toward profitable revenue growth.Ĭhris joins Lumen in an exciting time for our company and its stakeholders. Chris joined us from Arrow Electronics and brings over 25 years of finance leadership experience. ![]() I'd like to begin the call today by welcoming Chris Stansbury to the Lumen team as our chief financial officer. Thanks, Mike, and good afternoon, everyone, and thank you for joining us. Jeff Storey - President and Chief Executive Officer Before we begin, I need to call your attention to our safe harbor statement on Slide 2 of our first quarter 2022 presentation, which notes that this conference call may include forward-looking statements subject to certain risks and uncertainties. Joining me on the call today are Jeff Storey, president and chief executive officer Chris Stansbury, executive vice president and chief financial officer and our senior vice president and treasurer, Rafael Martinez-Chapman. Good afternoon, everyone, and thank you for joining us for the Lumen Technologies first quarter 2022 earnings call. Mike McCormack - Senior Vice President, Investor Relations It is now my pleasure to turn the conference over to Mike McCormack, senior vice president of investor relations. As a reminder, this conference is being recorded today, Wednesday, May 4, 2022. Greetings, and welcome to Lumen Technologies first-quarter earnings conference call. they are consistently closer to the reference image than the average distance of all individual manual segmentations.Lumen Technologies ( LUMN 1.29%) Q1 2022 Earnings Call May 04, 2022, 5:00 p.m. The results of this method are even better than manual segmentation, i.e. For pre-dilation images a fully automated active contour model, initialized by thresholding, preceded by filtering with a small-scale median filter is the best alternative for manual delineation. While for the post-dilation images no definite conclusions can be drawn, an automated contour model applied to images smoothed with a large kernel appears to be a good alternative to manual contouring. The evaluation has been carried out on 15 images, of which seven are obtained before balloon dilation and eight after balloon dilation. The results are compared with a reference image, obtained from manual editing, by use of four different quality parameters-two based on squared distances and two based on Mahalanobis distances. The combination of different filtering methods and object definition methods results in a total of 21 methods and 80 experiments. After a preprocessing step in which the catheter area is filled with lumen-like grey values, all approaches consist of two steps: (i) smoothing the images with different filtering methods and (ii) extracting the lumen by an object definition method. ![]() In this study, 21 different (semi-)automated binary-segmentation methods for determining the lumen are compared with manual segmentation to find an alternative for the laborious and subjective procedure of manual editing. Until now, no accurate, robust and reproducible method to obtain the lumen boundaries from intravascular ultrasound images has been described. The main quantities to be extracted from the data are the size and the shape of the lumen. One prerequisite for standard clinical use of intravascular ultrasound imaging is rapid evaluation of the data.
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