3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section location

Abstract

Background: Guidelines for localizing prostate cancer on imaging are ideally informed by registered post-prostatectomy histology. 3D histology reconstruction methods can support this by reintroducing 3D spatial information lost during histology processing. The need to register small, high-grade foci drives a need for high accuracy. Accurate 3D reconstruction method design is impacted by the answers to the following central questions of this work. (1) How does prostate tissue deform during histology processing? (2) What spatial misalignment of the tissue sections is induced by microtome cutting? (3) How does the choice of reconstruction model affect histology reconstruction accuracy?

Materials and methods: Histology, paraffin block face and magnetic resonance images were acquired for 18 whole mid-gland tissue slices from six prostates. 7-15 homologous landmarks were identified on each image. Tissue deformation due to histology processing was characterized using the target registration error (TRE) after landmark-based registration under four deformation models (rigid, similarity, affine and thin-plate-spline [TPS]). The misalignment of histology sections from the front faces of tissue slices was quantified using manually identified landmarks. The impact of reconstruction models on the TRE after landmark-based reconstruction was measured under eight reconstruction models comprising one of four deformation models with and without constraining histology images to the tissue slice front faces.

Results: Isotropic scaling improved the mean TRE by 0.8-1.0 mm (all results reported as 95% confidence intervals), while skew or TPS deformation improved the mean TRE by <0.1 mm. The mean misalignment was 1.1-1.9(°) (angle) and 0.9-1.3 mm (depth). Using isotropic scaling, the front face constraint raised the mean TRE by 0.6-0.8 mm.

Conclusions: For sub-millimeter accuracy, 3D reconstruction models should not constrain histology images to the tissue slice front faces and should be flexible enough to model isotropic scaling.

Keywords: 3D histology reconstruction; Correlative histopathology; image registration; prostate cancer imaging; validation.

Figure 1

Overview of the specimen processing, imaging and analysis

Figure 2

Schematic representations of tissue, landmarks and measurements, including (a) a surface rendering of a tissue slice magnetic resonance image, (b) a schematic rendering of the tissue slice with the front face fiducials f_i,j and the best fit front face plane F_j, (c) a projected side view of the tissue slice as oriented over the microtome blade with the front face fiducials f_i,j, the best fit front face plane F_j, the histology-visible landmarks m_{i, j}, the best fit histology section plane H_j, the orientation θ_j and the depth measurement d_j, (d) a schematic rendering of the tissue slice with the histology-visible landmarks and the best fit histology section plane H_j, (e) a schematic rendering of the paraffin block face after histological sectioning showing the homologous landmarks p_{i, j} and (f) a schematic rendering of the corresponding histology section with the homologous landmarks h_i,j

Figure 3

Illustrative examples of the T1-weighted tissue slice magnetic resonance (left), paraffin (middle) and histology (right) images transformed by a best-fit affine transformation aligning manually identified landmarks. The three highlighted regions in row 1 are shown magnified in rows 2, 3 and 4, with the corresponding landmarks denoted by arrows

Figure 4

Boxplot showing the target registration errors of homologous landmarks under four deformation models for the tissue deformation due to histological processing and cutting. These results correspond to the descriptive statistics shown in the first row of Table 1

Figure 5

Histograms of histology section depths and orientations. The subset of tissue slices illustrated in Figure 7 is shown in dark gray

Figure 6

Correlation of minimum, mean and maximum histology section depths with orientations. Tissue slices corresponding to sections marked with circles are shown in Figure 7

Figure 7

Renderings of the spatial relationships between tissue slices, histology-visible landmarks, front face plane and histology section planes for 10 tissue slices, ordered by increasing mean depth from left to right. Each tissue slice is shown as a silhouette projected along n(F_j) × n(H_j), the cross-product of the front face and histology section plane normals. With this projection, the front face plane F_j and the histology section plane Hj can be represented as solid and dashed lines respectively. The projected histology-visible landmarks m_{i, j} are shown as circles

Figure 8

3D reconstruction of three histology sections aligned (with a mean TRE of 0.5 mm) to an anterior view of 3D surface rendering of the corresponding intact ex vivo prostate gland with seminal vesicles, illustrating the potential for non-parallel, non-evenly spaced histological tissue sections

Figure 9

Sample sizes, relative to an arbitrarily chosen baseline, for imaging validation studies of the image signal differences between cancerous and background tissue for 0.2 cm³ cancer foci, under assumptions that foci are spherical and reconstruction error can be modeled as a translation error distributed as a 3D Gaussian and is combined in quadrature with a 2.1 mm TRE due to registration to in vivo imaging. Reconstructions under differing deformation assumptions and with or without the front face assumption are indicated, with the reconstruction using a similarity transform and the front face assumption arbitrarily chosen as the 100% baseline reference