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Computational Medical Imaging Analysis Chapter 7: Biomedical Applications

Computational Medical Imaging Analysis Chapter 7: Biomedical Applications. Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky Lexington, KY 40506. 7.1a: Neuronal Microanatomy and Function.

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Computational Medical Imaging Analysis Chapter 7: Biomedical Applications

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  1. Computational Medical Imaging Analysis Chapter 7: Biomedical Applications Jun Zhang Laboratory for Computational Medical Imaging & Data Analysis Department of Computer Science University of Kentucky Lexington, KY 40506 Chapter 7: CS689

  2. 7.1a: Neuronal Microanatomy and Function • Rapid growth of 3D visualization of microscopic structures happens with the advent of • Light and electron microscopy (classical) • Confocal microscopy • Atomic force microscopy • Tunneling microscopy Chapter 7: CS689

  3. 7.1b: Light and Electron Microscopy • Light microscopy images digitized directly from the microscope can provide a 3D volume image by incrementally adjusting the focal plane • It is usually followed by image processing to deconvolve the image to remove blurred, out-of-focus structures • Electron microscopy can generate multiple planes by controlling the depth of focus • Further processing is necessary for selective focal plane reconstruction Chapter 7: CS689

  4. 7.1b*: Light Microscopes Chapter 7: CS689

  5. 7.1b**: Electron Microscope ($69,000) Chapter 7: CS689

  6. 7.1c: Confocal Microscopy • Confocal microscopy uses incoherent light or laser with precise optical control to selectively image specific parallel sections within the microscopic structure • Multiple image planes can be selected, providing direct volume image acquisition without the need of signal from structures outside of the plane of interest • These images are often acquired using specific fluorescent dyes to selectively image a particular component of the structure under study Chapter 7: CS689

  7. 7.1c*: Confocal Microscope Chapter 7: CS689

  8. 7.1c**: Confocal Microscopy Images Chapter 7: CS689

  9. 7.1c**: Confocal Microscopy Images Chapter 7: CS689

  10. 7.1d: Neuron Visualization • The morphology and function of neurons from selected ganglia in the mammalian peripheral autonomic nervous system can be visualized • Information about a neuron’s shape and dimensions is needed to integrate and localize multiple synaptic inputs • The number and location of selective neurotransmitter receptor sites provides valuable information about the potential response of a neuron to a specific transmitter • Such visualization applications are termed as “spatial physiology” in which the function of microstructures are studies Chapter 7: CS689

  11. 7.1d*: Neuron Illustration Chapter 7: CS689

  12. 7.1d*: Single Neuron Chapter 7: CS689

  13. 7.1f: Imaging Neuron Architecture • Visualization of the architectural relationships between neurons is less well advanced • Nerve plexes, where millions of sensory nerve cells are packed into a few cubic millimeters of tissue, offer an opportunity to image a tractable number of cells in situ • This difficulty underscores the need for computer-assisted techniques to reconstruct neuronal architectures in vivo • They may not be visible directly from the images, but they can be visualized with assisting techniques Chapter 7: CS689

  14. 7.1f*: Rat Neuron Chapter 7: CS689

  15. 7.2a: Corneal Cell Analysis • The density and arrangement of corneal cells is an indicator of the general health of the cornea • These factors are routinely evaluated to determine suitability for transplant • The corneal confocal microscope is a reflected-light scanning aperture microscope fitted for direct contact with a living human cornea • The image is a 3D tomographic optical image of the cornea • Algorithms are developed for automated measurement of local keratocyte nuclear density in the cornea Chapter 7: CS689

  16. 7.2b: Human Cornea Chapter 7: CS689

  17. 7.2c: Local Keratocyte Density • The sectional images represent a section about 15 microns thick and at 1 micron intervals through the entire depth of the cornea • Both global and local automated density counts in rabbit corneas correlate well to those obtained from conventional histologic evaluation of cornea tissue • A decrease in keratocyte density toward the posterior of the cornea was found Chapter 7: CS689

  18. 7.2d: Keratocyte Density Images Left: Corneal confocal image. Right: Nuclei counting Chapter 7: CS689

  19. 7.2e: In Vivo Study of Cornea Density In vivo confocal microscopy images show the presence of densely packed ovoid or elliptical cell bodies, decreasing after birth for a neonate Chapter 7: CS689

  20. 7.2f: Cornea Density of Neonate Laser scanning micrographs of neonatal corneas show decreasing cell density after birth, confirming the in vivo confocal microscopy images Chapter 7: CS689

  21. 7.3a: Trabecular Tissue Analysis in Glaucoma • The trabecular tissue of the eye is a ring of spongy, fluid-filled tissue situated at the junction of cornea, iris, and sclera • This tissue lies in the only outflow path for aqueous humor, it has long been implicated in the eye disease glaucoma • The architecture of the trabecular tissue is so complex that most studies have focused on the architecture of the connected fluid space Chapter 7: CS689

  22. 7.3a*: Trabecular Tissue Image Chapter 7: CS689

  23. 7.3b: Connected Fluid Space Analysis • The fluid space is generally continuous from the anterior chamber through the trabecular tissue into Schlemm’s canal • Morphometric analysis (in which small chambers were successively closed) revealed that the interconnection is maintained by very small chambers • There are a large number of these narrowings, and they occur at all regions of the tissue Chapter 7: CS689

  24. 7.3c: Connected Fluid Space in Human Trabecular Tissue Before (left) and after (right) morphological opening Chapter 7: CS689

  25. 7.4a: Prostate Microvessels • It is common practice to surgically remove cancerous prostates, even though subsequent pathological examination of excised tissues suggest that some surgeries could have been avoided • There is a great need for improved non-invasive preoperative techniques that can more accurately measure tumor volume and extent • The measures of prostate tumor size and microvessel density are useful indicators of the metastatic potential of tumor Chapter 7: CS689

  26. 7.4a*: Prostate Cancer Chapter 7: CS689

  27. 7.4b: 3D Visualization of Microvessels • 3D image analyses show that the ratio of gland volume to vessel length exhibits a twofold increase between benign and malignant tumors • The normal tissue shows a characteristic circumferential pattern of the microvessels relative to the glandular tissue • In region with adenocarcinoma, the pattern of microvessels is tortuous and radically diffused throughout the glandular volume Chapter 7: CS689

  28. 7.4b**: Tumor & Neovasculature Chapter 7: CS689

  29. 7.4b*: Frog Microvessel Chapter 7: CS689

  30. 7.4c: Measurements of Microvessels • Neovasculature exhibits a statistically significantly larger standard deviation of curvature than the normal vessels • These measurements can be done with the images • Volume of tissue required for the histologic analysis is similar to that obtained via needle biospy • 3D image with biospy sample provides a marker for presurgical stage and outcome, improve patient population stratification and eliminate unnecessary surgeries Chapter 7: CS689

  31. 7.4c*: Stages of Prostate Cancer Chapter 7: CS689

  32. 7.5a: Prostate Surgery Planning • Radical prostatectomy is the most commonly performed surgical procedure • The procedure has significant morbidity • Minimizing these negative affects needs a careful balance between completely removal of all cancerous prostate tissue and sparing neural and vascular structures • Routine surgical rehearsal using patient specific data could have significant effect on procedural success Chapter 7: CS689

  33. 7.5b: Prostate Cancer Surgery Chapter 7: CS689

  34. 7.5c: Presurgical Rehearsal • Presurgical MR volume images of patients scanned with a rectal coil can be segmented to identify and locate the prostate, bladder, and other tissues • The segmented images can be constructed into faithful patient-specific models and reviewed by surgeons interactively before the surgery • The approach, margins, and critical tradeoffs can be evaluated and determined upon seeing the pathology localized relative to normal anatomy • Rendered views of patient-specific models of prostate cancer can be used to accurately assess the tumor size and location relative to sensitive structures Chapter 7: CS689

  35. 7.5d: Prostate Surgical Planning Chapter 7: CS689

  36. 7.6a: Craniofacial Surgery Planning and Evaluation • Craniofacial surgery (CFS) involves surgery of the facial and cranial skeleton and soft tissues • Preoperative information is most often acquired using X-ray CT scanning for the bony structures, with MRI used for imaging the soft internal tissues • 3D visualization facilitates accurate measurement of structures of interest, allowing precise design of surgical procedures • It also minimizes the duration of surgery, reducing the risk of postoperative complication and cost Chapter 7: CS689

  37. 7.6b: Craniofacial Surgery (I) Chapter 7: CS689

  38. 7.6c: Craniofacial Surgery (II) Chapter 7: CS689

  39. 7.6d: Craniofacial Surgery Planning Chapter 7: CS689

  40. 7.6e: Craniofacial Reconstruction (I) Chapter 7: CS689

  41. 7.6f: Craniofacial Reconstruction (II) Chapter 7: CS689

  42. 7.7a: Neurosurgery Planning • Neurosurgery needs extended knowledge and understanding of intricate relationships between normal anatomy and pathology • Multimodality scans are coregistered to help neurosurgeon understand anatomy of interest • Specific anatomical objects may be identified and segmented, creating object maps within the digital volumetric dataset • The diagnostic information is used to determine the margins of pathology, to avoid critical structures, e.g., cerebral vasculature and eloquent cortical tissue Chapter 7: CS689

  43. 7.7a*: Virtual Surgery Planning Chapter 7: CS689

  44. 7.7b: Neurosurgery Planning in Epilepsy Chapter 7: CS689

  45. 7.7c: Neurosurgery Planning in Tumor Resection Chapter 7: CS689

  46. 7.7d: Neurosurgery (I) Chapter 7: CS689

  47. 7.7e: Neurosurgery (II) Chapter 7: CS689

  48. 7.7f: Neurosurgery (III) Chapter 7: CS689

  49. 7.7g: Intraoperative Guidance • Interactive computation of line-of-sight oblique planar images for planning neurosurgical approach to large tumor • Neurosurgeon will have direct visualization of image planes along the path of surgical approach • T1-weighted MRI prior to contrast enhancement (2nd row), T1-wieghted MRI with gadolinium to define tumor size (3rd row), MR angiogram to localize position of important vessels (4th row) Chapter 7: CS689

  50. 7.7g*: Neurosurgery (IV) Chapter 7: CS689

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