Distance Visualization for Interactive 3D Implant Planning

Abstract

Fig. 1. Distance visualization for interactive (> 30 fps) preoperative implant planning using color coding on the implant surface (left), oriented distance glyphs (middle), and color coding on the implant surface and on a set of additional axial slices (right).Abstract-An instant and quantitative assessment of spatial distances between two objects plays an important role in interactive applications such as virtual model assembly, medical operation planning, or computational steering. While some research has been done on the development of distance-based measures between two objects, only very few attempts have been reported to visualize such measures in interactive scenarios. In this paper we present two different approaches for this purpose, and we investigate the effectiveness of these approaches for intuitive 3D implant positioning in a medical operation planning system. The first approach uses cylindrical glyphs to depict distances, which smoothly adapt their shape and color to changing distances when the objects are moved. This approach computes distances directly on the polygonal object representations by means of ray/triangle mesh intersection. The second approach introduces a set of slices as additional geometric structures, and uses color coding on surfaces to indicate distances. This approach obtains distances from a precomputed distance field of each object. The major findings of the performed user study indicate that a visualization that can facilitate an instant and quantitative analysis of distances between two objects in interactive 3D scenarios is demanding, yet can be achieved by including additional monocular cues into the visualization.

A Systematic Review of Experimental Studies on Data Glyphs

“…Thus, we do not highlight this group as a category in our result table. The two studies we found ( [32] and [33]) are, however, discussed throughout the paper.…”

“…Patients rated by psychiatrists [21] Minnesota Multiphasic Personality Inventory Medical data [32] unknown Medical images [56] unknown Cars dataset [34] http://davis.wpi.edu/ xmdv/datasets/cars.html Project plans [35] unknown MM5 weather information [36] unknown Weather information [37] NCEP forecast model Weather information [42] Operational Regional Atmospheric Prediction System Modified U.S. census data [38] unknown Financial data [40] Investment in education USA (2008) Financial data [84] Wall Street Journal Index (1974 and 1975) Financial data [85] Standard and Poor's firm list (1974 and 1975) Classical music data [41] unknown k data [43] Lostpedia wiki edits Network data [44] 10 best ranked movies (IMDb) Google search results [45] http://www.google.de Marathon runners [50] unknown Power plant statistics [53] unknown Audio information [62] One laptop per child sound library Biological data [70] unknown Economic variables [77] U.S. Department of commerce & labor Tensor data [92] DTI dataset possible confounding factors, so as to better reason about changes in performance when modifying specific aspects of the same design [91], or when comparing it with other representations [48], [49], [50], [54], [64], or layouts [55].…”

“…Study missing mappings: In Table 3 we refer to 50 of the 64 studies examined, having left out the two one-to-many mappings [32], [33] and the twelve that were not compared to other designs [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46]. Looking at the table there is still clearly an imbalance in what kind of data encodings have been comparatively tested.…”

Thickness and clearance visualization based on distance field of 3D objects

“…Similarly, in the medical field, some clearance visualization methods are known to assist in the navigation of implants in virtual environments [17,18]. These methods are too specialized for simply visualizing the clearance distance around objects.…”

Visual Comparison of 3D Medical Image Segmentation Algorithms Based on Statistical Shape Models

“…They do not discriminate between two results which are badly segmented in different regions (e.g., top and bottom of an organ). Visual inspection concerns individual instances shown in 2D and 3D views such as ITK Snap (www.itksnap.org) or overlay the two meshes using transparency, color-coded local distances between vertices, e.g., [6,2,5,13]. These views are very detailed, but require manual inspection of all instances individually.…”