PD42-03 ROBUST AUTOMATIC RENAL STONE DETECTION IN ULTRASONIC LIVE STREAMS FOR IMPROVING EXTRACORPOREAL SHOCK WAVE THERAPY

Abstract

You have accessJournal of UrologyStone Disease: New Technology1 Apr 2015PD42-03 ROBUST AUTOMATIC RENAL STONE DETECTION IN ULTRASONIC LIVE STREAMS FOR IMPROVING EXTRACORPOREAL SHOCK WAVE THERAPY Werner Pomwenger, Peter Ott, Stefan Wegenkittl, Reinhold Zimmermann, Olaf Gleibe, and Axel Koch Werner PomwengerWerner Pomwenger More articles by this author , Peter OttPeter Ott More articles by this author , Stefan WegenkittlStefan Wegenkittl More articles by this author , Reinhold ZimmermannReinhold Zimmermann More articles by this author , Olaf GleibeOlaf Gleibe More articles by this author , and Axel KochAxel Koch More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2588AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES As the gold standard, Extracorporeal Shock Wave Lithotripsy (ESWL) predominantly uses X-ray as a localization system. Ultrasound (US) for stone detection is an alternative method allowing for continuous assessment during the treatment while avoiding radiation exposure. Despite its evident advantages, comparably lower image quality and staff training costs have been identified as major barriers against a wider acceptance. We aim to develop a reliable automatic US-based renal stone detection system to support urologists in ESWL treatment by accurately locating targets in real-time. METHODS Our prototype combines i) a single, frame-based multistage candidate recognition system with ii) a multiple frame-based probabilistic tracking. For i), image enhancement is followed by feature extraction to identify stone candidates. Subsequently, these are examined for the presence of shadowing effects and classified using a finite state machine. The high detection rate (52% to 78%) is achieved by ii) posterior probabilities based on accumulated stone evidence in successive US frames in order to substantially increase sensitivity. To demonstrate robustness, the prototype is evaluated against a set of 5 recorded US sequences (Aloka 3500SX, 3.5MHz transducer, average 60 sec, 17 fps ), annotated by a medical expert, well-trained in US-based stone diagnosis. RESULTS The recorded video sequences showed a strongly varying image quality and typical artefacts (e.g. speckle, ring down, rib shadow or electronic spiking), which was intended as it reflects the real case of clinical application. The correct detection rates vary from 61.7 – 95.0% with sensitivity being from 96.4 – 98.1% with respect to the established ground truth. In this context the positive predictive value was 86.8% in average. CONCLUSIONS As future medical guidelines are expected to call for higher avoidance of radiation exposure of patients, the use of US in ESWL will become desirable. To bring US on a par with X-ray based location, it will strongly benefit from an automatic stone detection system. By combining single- and multi-frame features of US and analyzing these in a decision logic, a substantial increase in stone detection rates was achieved. The moderate to low US video quality used in the tests raises our expectations with respect to the currently ongoing clinical evaluation. With this real-time system US-only based ESWL will perform better in regards of radiation hygiene, stone identification, focus accuracy and treatment efficiency. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e886 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Werner Pomwenger More articles by this author Peter Ott More articles by this author Stefan Wegenkittl More articles by this author Reinhold Zimmermann More articles by this author Olaf Gleibe More articles by this author Axel Koch More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...