Bladder mapping

Abstract

Aspects of bladder mapping are described herein. According to one aspect, an exemplary method comprises: generating, with an imaging element, a video feed depicting a bladder wall; establishing, with a processor, markers on the bladder wall in the video feed; tracking, with the processor, relative movements between the markers; and/or identifying, with the processor, a location of a contraction of the bladder wall based on the relative movements. Associated devices and systems also are described.

1 - 20 . (canceled) 
     
     
         21 . A method comprising:
 generating, with an imaging element, a live video feed depicting a muscle wall;   establishing, with a processor, virtual markers on the muscle wall in the video feed;   identifying, with the processor, a location of a contraction of the muscle wall based on relative movements between the virtual markers; and   determining, with the processor, characteristics of the identified contraction of the muscle wall.   
     
     
         22 . The method of  claim 21 , wherein establishing the virtual markers comprises:
 locating in a first frame of the video feed, with the processor, a natural feature of the muscle wall;   assigning, with the processor, a first virtual marker to the natural feature in the first frame;   locating in a second frame of the video feed, with the processor, the natural feature on the muscle wall; and   assigning, with the processor, a second virtual marker to the natural feature in the second frame.   
     
     
         23 . The method of  claim 22 , wherein:
 establishing the virtual markers further comprises generating, with the processor, a first binary image of the first frame and a second binary image of the second frame;   each of the first and second binary images include data points defining a synthetic geometry of the natural feature; and   the locating and assigning steps further comprise:
 locating in the first and second binary images, with the processor, the location of the natural feature by generating a correlation between a reference pattern and data points from the synthetic geometry; and 
 assigning, with the processor, the first and second virtual markers to the data points based on the correlation. 
   
     
     
         24 . The method of  claim 21 , wherein identifying the location of the contraction comprises:
 establishing, with the processor, a tracking area of the muscle wall in the first and second frames that includes the first and second virtual markers;   analyzing, with the processor, relative movements between the first and second virtual markers in the tracking area to determine one or more movement vectors; and   analyzing the one or more movement vectors.   
     
     
         25 . The method of  claim 24 , wherein identifying the location of the contraction further comprises:
 locating, with the processor, a center of movement for the one or more movement vectors; and   determining, with the processor, a magnitude of each movement vector and a distance from the center of movement for each movement vector.   
     
     
         26 . The method of  claim 21 , wherein identifying, with the processor, the location of the contraction of the muscle wall occurs while still generating the live video feed depicting the muscle wall with the established virtual markers. 
     
     
         27 . The method of  claim 21 , wherein the characteristics of the identified contraction include at least one of a contraction strength, a contraction frequency, a contraction profile, a contraction duration, and a contraction density. 
     
     
         28 . The method of  claim 21 , further comprising diagnosing, with the processor, a condition of the muscle wall based on the characteristics of the contraction. 
     
     
         29 . The method of  claim 21 , further comprising:
 monitoring, with a sensor, characteristics of the muscle wall;   generating, with the processor, a correlation between the characteristics of the contraction and the characteristics of the muscle wall; and   diagnosing, with the processor, the condition of the muscle wall based on at least one of the characteristics of the contraction, the characteristics of the muscle, and the correlation therebetween.   
     
     
         30 . The method of  claim 29 , wherein the muscle wall is a bladder wall, and the characteristics of the bladder wall include at least one of a fluid pressure applied to the bladder wall and a volume of fluid retained by the bladder wall. 
     
     
         31 . The method of  claim 30 , wherein a contraction strength of the bladder is correlated with the fluid pressure to distinguish an overactive bladder from other conditions. 
     
     
         32 . A method comprising:
 locating in frames of a live video feed, with a processor, a natural feature of a muscle wall depicted in the video feed;   assigning, with the processor, virtual markers to the natural feature in each frame of the video feed;   establishing, with the processor, a tracking area of the muscle wall in the video feed including first and second virtual markers;   analyzing, with the processor, relative movements of the virtual markers in the tracking area to determine one or more movement vectors and a center of movement for the one or more movement vectors, wherein the analysis is in real-time as each of the relative movements occur in the video feed; and   qualifying, with the processor, a movement of the muscle wall as a contraction based on a ratio between the magnitude of each movement vector and a distance from the center of movement for each movement vector.   
     
     
         33 . The method of  claim 32 , wherein assigning the virtual markers comprises:
 locating in at least a first frame of the video feed, with the processor, a natural feature of the muscle wall;   assigning, with the processor, the first virtual marker to the natural feature in at least the first frame;   locating in at least a second frame of the video feed, with the processor, the natural feature on the muscle wall; and   assigning, with the processor, the second virtual marker to the natural feature in at least the second frame.   
     
     
         34 . The method of  claim 32 , further comprising:
 generating, with the processor, a binary image of each frame in the video feed, each binary image including data points defining a synthetic geometry of the muscle wall;   locating in each binary image, with the processor, the natural feature by generating a correlation between a reference pattern and data points from the synthetic geometry; and   assigning, with the processor, the first and second virtual markers to the data points based on the correlation.   
     
     
         35 . The method of  claim 32 , further comprising:
 identifying, with the processor, the location of each qualified contraction based on the one or more movement vectors;   outputting, with the processor, the video feed to a display device; and   overlaying onto the video feed, with the processor, the location of each qualified contraction, and characteristics of each qualified contraction including at least one of a contraction strength, a contraction frequency, a contraction profile, a contraction duration, and a contraction density.   
     
     
         36 . The method of  claim 35 , further comprising overlaying, with the processor, indicators onto each frame in the video feed. 
     
     
         37 . A method comprising:
 selecting from a video feed, with a processor, a first frame depicting a muscle wall, and a second frame depicting the muscle wall;   generating, with the processor, a first binary image of the first frame and a second binary image of the second frame;   locating in the first and second binary images, with the processor, one or more natural features of the muscle wall;   assigning, with the processor, a first virtual marker relative to the one or more natural features in the first frame, and a second virtual marker relative to the one or more natural features in the second frame;   determining, with the processor, a location of one or more contractions of the muscle wall based on relative movements between the first and second virtual markers in real-time, by the processor analyzing the video-feed; and   selecting, with the processor, a location of the muscle wall for a treatment based on the location of one or more contractions.   
     
     
         38 . The method of  claim 37 , further comprising:
 determining, with the processor, characteristics of the treatment; and   applying the treatment to the selected location.   
     
     
         39 . The method of  claim 37 , wherein the selection of the location of the muscle wall for the treatment is based on characteristics of the one or more contractions, the characteristics including at least one of a contraction strength, a contraction frequency, a contraction profile, a contraction duration, and a contraction density. 
     
     
         40 . The method of  claim 38 , wherein the treatment includes a wave energy, and the characteristics of the treatment include at least one of an intensity, power level, duration, and pulsing.