WO2012037091A1 - Préservation de caractéristiques lors du déploiement d'un côlon - Google Patents

Préservation de caractéristiques lors du déploiement d'un côlon Download PDF

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Publication number
WO2012037091A1
WO2012037091A1 PCT/US2011/051351 US2011051351W WO2012037091A1 WO 2012037091 A1 WO2012037091 A1 WO 2012037091A1 US 2011051351 W US2011051351 W US 2011051351W WO 2012037091 A1 WO2012037091 A1 WO 2012037091A1
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Prior art keywords
interest
colon
rays
centerline
spline
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PCT/US2011/051351
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English (en)
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WO2012037091A4 (fr
Inventor
Wei Hong
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Siemens Corporation
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Publication of WO2012037091A1 publication Critical patent/WO2012037091A1/fr
Publication of WO2012037091A4 publication Critical patent/WO2012037091A4/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T15/00—3D [Three Dimensional] image rendering
    • G06T15/08—Volume rendering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/30—Polynomial surface description
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/06—Topological mapping of higher dimensional structures onto lower dimensional surfaces
    • G06T3/067—Reshaping or unfolding 3D tree structures onto 2D planes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/149—Segmentation; Edge detection involving deformable models, e.g. active contour models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G06T7/41—Analysis of texture based on statistical description of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2210/00—Indexing scheme for image generation or computer graphics
    • G06T2210/41—Medical
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2215/00—Indexing scheme for image rendering
    • G06T2215/06—Curved planar reformation of 3D line structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2219/00—Indexing scheme for manipulating 3D models or images for computer graphics
    • G06T2219/021—Flattening

Definitions

  • the present disclosure relates to image editing, and more particularly to a volumetric data unfolding method.
  • Colorectal cancer is a leading cause of cancer related deaths. Colorectal cancer accounts for approximately 945,000 new cases and 500,000 deaths worldwide each year. Most colorectal cancers begin as a polyp, which is a small, harmless growth in the wall of the colon. As a polyp gets larger, it can develop into a cancer that grows and spreads. Early detection of colon cancer is the key ot a good prognosis. It can take from 10 to 15 years for an adenomatous polyp to become an invasive cancer. Thus, there is a considerable time for detection and clinical intervention if the proper screening methods are used.
  • VC virtual colonoscopy
  • CTC computed tomographic colonography
  • a volumetric data unfolding method includes applying a histogram based intensity classification to a volumetric data including an object of interest for identifying the object of interest and a material in the object of interest, segmenting the object of interest from the volumetric data, determining a centerline of the object of interest, casting a plurality of rays from the centerline to determine a surface of the object of interest, wherein the plurality of rays ignore the material in the object of interest, resampling the centerline at a plurality of sampling points along the plurality of rays, fitting the surface of the object of interest with B-splines using the sampling points to determine a B-spline surface, wherein topological noise is filtered, determining a feature of the B-spline surface to be preserved, and unfolding surface of the object of interest to determine a planar surface while preserving the feature by determining coordinates for a plurality of vertices of the B- spline surface and mapping the surface of the B-
  • a volumetric data unfolding method includes applying a histogram based intensity classification to a volumetric data including an object of interest for identifying the object of interest and a material in the object of interest, segmenting the object of interest from the volumetric data, determining a centerline of the object of interest, casting a plurality of rays from the centerline to determine a surface of the object of interest, wherein the plurality of rays ignore the material in the object of interest, resampling the centerline at a plurality of sampling points along the plurality of rays, determining a feature of the surface to be preserved, and unfolding surface of the object of interest to determine a planar surface while preserving the feature by determining coordinates for a plurality of vertices of the B-spline surface and mapping the surface of the B-spline surface to a planar surface.
  • a volumetric data unfolding system includes a processor configured to cast a plurality of rays from a centerline of an object of interest to determine a surface of the object of interest, wherein the plurality of rays ignore at least one type of tagged material in the object of interest, the processor further configured to resample the centerline at a plurality of sampling points along the plurality of rays, the processor further configured fit the surface of the object of interest with B-splines using the sampling points to determine a B-spline surface, wherein topological noise is filtered, the processor further configured determine a feature of the B-spline surface to be preserved, and the processor further configured unfold surface of the object of interest to determine a planar surface while preserving the feature by determining coordinates for a plurality of vertices of the B-spline surface and mapping the surface of the B-spline surface to a planar surface, and a memory configured to store the unfolded surface.
  • FIG. 1 A is a flow chart showing a method for unfolding image data according to an embodiment of the present disclosure
  • FIG. IB is a flow chart showing a method for unfolding image data according to an embodiment of the present disclosure
  • FIG. 1C is a flow chart showing a method for unfolding image data according to an embodiment of the present disclosure.
  • FIG. 2 is a diagram of a system for unfolding image data according to an embodiment of the present disclosure.
  • an inner surface of a colon lumen may be virtually unfolded from CT data, even when fecal tagging CT data is used.
  • an exemplary volumetric data unfolding method does not require the input data to be clean. It uses a ray casting method to find the inner colon surface even when there are remnants of stool and residual floods inside the colon lumen.
  • an exemplary volumetric data unfolding method may generate a topology simple colon surface for stable colon unfolding.
  • a topology simple surface is free of topology noise, such as that typically generated by a marching cube method.
  • the topology noise is expressed as tunnels or handles in the 3D triangle mesh.
  • an exemplary volumetric data unfolding method can reduce distortion and preserve detected features, such as potential polyps.
  • an exemplary volumetric data unfolding method allows a user to inspect the whole inner colon surface on a single 2D image.
  • air inside the colon, soft tissue, and tagged colonic materials are conservatively identified using a histogram based intensity classification 101. These elements, e.g., air inside the colon, soft tissue, and tagged colonic materials, may be classified in the volumetric data.
  • these elements e.g., air inside the colon, soft tissue, and tagged colonic materials, may be classified in the volumetric data.
  • “conservatively” means that voxels that may belong to more than one element according to a given metric or expert opinion are classified as UNKNOWN.
  • a voxel in this range may be a soft tissue or tagged material, and thus may be tagged UNKNOWN.
  • Each voxel is labeled as air, soft tissue, or tagged colonic material.
  • the colon lumen is roughly segmented for centerline extraction 102. It should be noted that not all voxels may be classified and that the segmentation result is an approximation of the colon lumen. An accurate segmentation of colon lumen, in which all voxels are classified, is not necessary. That is, the subsequent colon unfolding result may not rely on this
  • a centerline of the colon is extracted from the segmented colon lumen 103.
  • the centerline may be used to provide camera position and orientation for inner colon surface sampling. Any centerline extraction method may be used.
  • the colon unfolding result may not rely on the centerline extraction.
  • the centerline is uniformly re-sampled and at each sampling position, and a number of rays are cast to detect the inner colon surface 104.
  • a number of points on the centerline may be chosen to cast rays.
  • uniformly means the distance between neighboring points is same.
  • Each ray stops at the inner colon surface and returns a 3D position of a last sampling point.
  • Each ray will pass tagged material and stop when it hits the colon surface by using multiple sampling points along the ray.
  • sampling points may then be used to fit a surface with B-splines 105, by which the topological noise can be filtered.
  • These sampling points obtained from the ray casting are assumed to be on the colon surface.
  • a B-spline surface can be fitted (or determined), wherein all the sampling points are on the B-spline surface/colon surface.
  • Features such as shape index and curvedness, may be determined using the surface determined by the B-splines (the B-spline surface) 106.
  • the feature information may be used to determine which part of the colon surface is to be preserved during optimization and may not be used for polyp detection. Boundary conditions and constraints based on the calculated features are placed on the fitted colon surface.
  • a harmonic function may be determined on the colon surface, which may be used to determine 2D coordinates for all vertices of the colon surface 107.
  • the colon surface is mapped to a planar surface, which can be displayed as a single 2D image and inspected by the physicians.
  • the ray casting 104 may be combined with surface fitting 105 to generate an inner colon surface and/or to solve a topological noise problem at block 108 as shown in FIG. IB. That is, the fitted colon surface has a B-spline representation, on which features can be determined.
  • the topological noise problem may be an isosurface correction method to detect and remove handles from a mesh representing the colon surface.
  • the processing and rendering can be executed on a multi-core machine where an additional data transfer between a central processor unit (CPU) and a graphics processor unit (GPU) is avoided.
  • CPU central processor unit
  • GPU graphics processor unit
  • the colon wall can be extracted accurately and a
  • segmentation mask can be used in the ray casting 104 for inner colon surface sampling.
  • the segmentation mask may be a binary volume having the same size as the original data.
  • Voxels of a segmentation result e.g., the inner colon surface determined by the ray casting 104, may be characterized as belonging to the segmentation mask or background voxels.
  • features can be calculated from the original CT data instead of the fitted colon surface as shown in FIG. 1C.
  • Embodiments of the present disclosure may be implemented to other types of volumetric data, including X-ray data, Magnetic Resonance Imaging (MRI) data, etc. It is to be understood that embodiments of the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processors, or a combination thereof.
  • a software application program is tangibly embodied on a non-transitory computer-readable storage medium, such as a program storage device or computer program product, with an executable program stored thereon.
  • the application program may be uploaded to, and executed by, a machine comprising any suitable architecture.
  • a computer system (block 201) for performing a volumetric data unfolding method includes, inter alia, a CPU (block 202), a memory (block 203) and an input/output (I/O) interface (block 204).
  • the computer system (block 201) is generally coupled through the I/O interface (block 204) to a display (block 205) and various input devices (block 206) such as a mouse, keyboard and a slide-scanning microscopy X-Y stage.
  • the support circuits can include circuits such as cache, power supplies, clock circuits, and a communications bus.
  • the memory (block 203) can include random access memory (RAM), read only memory (ROM), disk drive, tape drive, etc., or a combination thereof.
  • RAM random access memory
  • ROM read only memory
  • disk drive disk drive
  • tape drive etc.
  • the computer system (block 201) is a general purpose computer system that becomes a specific purpose computer system when executing the routine of the present disclosure.
  • the computer platform may include a GPU 209 for processing the image data 208.
  • the GPU 209 may be part of a graphics card 210 with dedicated memory 211.
  • the computer platform (block 201) also includes an operating system and micro instruction code.
  • the various processes and functions described herein may either be part of the micro instruction code or part of the application program (or a combination thereof) which is executed via the operating system.
  • various other peripheral devices may be connected to the computer platform such as an additional data storage device and a printing device.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Computer Graphics (AREA)
  • Quality & Reliability (AREA)
  • Pure & Applied Mathematics (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Medical Informatics (AREA)
  • Algebra (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Geometry (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Hardware Design (AREA)
  • General Engineering & Computer Science (AREA)
  • Probability & Statistics with Applications (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
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Abstract

Selon l'invention, un procédé de déploiement de données volumétriques inclut l'application à des données volumétriques incluant un objet présentant intérêt d'un classement d'intensités fondé sur un histogramme afin d'identifier l'objet présentant intérêt et une matière dans l'objet présentant intérêt (101), la segmentation de l'objet présentant intérêt à partir des données volumétriques (102), la détermination d'un axe de l'objet présentant intérêt (103), la projection d'une pluralité de rayons à partir de l'axe afin de déterminer la surface de l'objet présentant intérêt (104), la pluralité de rayons provenant de l'axe ignorant le matériau dans les objets présentant intérêt, le ré-échantillonnage de l'axe au niveau d'une pluralité de points d'échantillonnage le long de la pluralité de rayons, l'adaptation de la surface de l'objet présentant intérêt avec des splines B en utilisant les points d'échantillonnage pour déterminer une surface de splines B (105), le bruit d'ordre topologique étant filtré, la détermination d'une caractéristique de la surface de splines B à préserver (106) et la surface de déploiement de l'objet présentant intérêt (107) afin de déterminer une surface plane tout en préservant la caractéristique en déterminant des coordonnées pour une pluralité de sommets de la surface de splines B et en mappant la surface formant la surface des splines B en une surface plane, une surface dépliée de l'objet présentant intérêt étant affichée comme image en deux dimensions.
PCT/US2011/051351 2010-09-17 2011-09-13 Préservation de caractéristiques lors du déploiement d'un côlon WO2012037091A1 (fr)

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US61/383,893 2010-09-17

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Cited By (2)

* Cited by examiner, † Cited by third party
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EP3404622A1 (fr) * 2017-05-19 2018-11-21 Siemens Healthcare GmbH Procédé de traitement de données d'image
US20200388034A1 (en) * 2015-12-31 2020-12-10 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for image processing

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200388034A1 (en) * 2015-12-31 2020-12-10 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for image processing
US11769249B2 (en) * 2015-12-31 2023-09-26 Shanghai United Imaging Healthcare Co., Ltd. Systems and methods for image processing
EP3404622A1 (fr) * 2017-05-19 2018-11-21 Siemens Healthcare GmbH Procédé de traitement de données d'image
US11367523B2 (en) 2017-05-19 2022-06-21 Siemens Healthcare Gmbh Method for image data processing

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