Three‐dimensional volume reconstruction of extracellular matrix proteins in uveal melanoma from fluorescent confocal laser scanning microscope images

Abstract

SummaryThe distribution of looping patterns of laminin in uveal melanomas and other tumours has been associated with adverse outcome. Moreover, these patterns are generated by highly invasive tumour cells through the process of vasculogenic mimicry and are not therefore blood vessels. Nevertheless, these extravascular matrix patterns conduct plasma. The threedimensional (3D) configuration of these laminin-rich patterns compared with blood vessels has been the subject of speculation and intensive investigation. We have developed a method for the 3D reconstruction of volume for these extravascular matrix proteins from serial paraffin sections cut at 4 µm thicknesses and stained with a fluorescently labelled antibody to laminin (Maniotis et al., 2002). Each section was examined via confocal laser-scanning focal microscopy (CLSM) and 13 images were recorded in the Z-dimension for each slide. The input CLSM imagery is composed of a set of 3D subvolumes (stacks of 2D images) acquired at multiple confocal depths, from a sequence of consecutive slides. Steps for automated reconstruction included (1) unsupervised methods for selecting an image frame from a subvolume based on entropy and contrast criteria, (2) a fully automated registration technique for image alignment and (3) an improved histogram equalization method that compensates for spatially varying image intensities in CLSM imagery due to photo-bleaching. We compared image alignment accuracy of a fully automated method with registration accuracy achieved by human subjects using a manual method. Automated 3D volume reconstruction was found to provide significant improvement in accuracy, consistency of results and performance time for CLSM images acquired from serial paraffin sections.

Trajectory fusion for three-dimensional volume reconstruction

“…However, in the case of CLSM data, one could use rather a deterministic and computationally simpler method than RANSAC [17]. This method had been evaluated in the past on CLSM data for feature matching [5] and performs sufficiently well. The method establishes feature correspondence by solving the Procrustes problem [18] using two-step matching method, such as Euclidian distance based method followed by vector distance based method.…”

“…First, the end-point duplication is performed by replicating (x,y) coordinate of the adjacent end trajectory points with physical gap Δ k between and as follows: (5) An example of trajectory fusion by end-point duplication is illustrated in Figure 5. Second, the polynomial-based extrapolation is performed by estimating the polynomial functions and with degree γ based on and respectively.…”

“…Intensity-based methods are typically performed by selecting or generating a pair of representative images from adjacent sub-volumes [5], determining a global transformation parameters by minimizing a similarity metric for all possible transformations, and applying the computed global transformation to the sub-volume. Intuitively, using the intensity-based approach, one could select a pair of end frames that are the closest to the boundary of adjacent physical sections to minimize morphological distortion of the structural changes.…”

“…However, it is well known that due to spatial intensity heterogeneity of end frames in sub-volumes, correlation techniques would result in very low similarity measures, which frequently leads to undesirable sensitivity to noise and inaccurate alignments [6], [7]. To overcome the problems with end frames, alternative approaches for frame selection have been suggested that would select the highest contrast images [5] or generate a representative image based on sub-volume analysis, e.g., projection used in [8]. Although the intensity-based methods are successful in a limited set of problems, it should be noted that they are computationally expensive due to a large search space of transformation parameters.…”

Automatic alignment of stacks of filament data

“…However, the resolution that can be obtained is limited by the thickness of the tissue, because of light scattering. Thick tissue can be imaged by first cutting the specimen into thin sections, which are mounted and optically sectioned individually to produce a stack of thin, but three-dimensional subvolumes [1,2]. When using electron tomography instead of light microscopy, individual cells can be studied at even smaller scale [3].…”