The registration of two-dimensional histology images to reference images from other modalities is an important preprocessing step in the reconstruction of three-dimensional histology volumes. This is a challenging problem because of the differences in the appearances of histology images and other modalities, and the presence of large nonrigid deformations which occur during slide preparation. This paper shows the feasibility of using densely sampled scale-invariant feature transform (SIFT) features and a SIFTFlow deformable registration algorithm for coregistering whole-mount histology images with blockface optical images. We present a method for jointly optimizing the regularization parameters used by the SIFTFlow objective function and use it to determine the most appropriate values for the registration of breast lumpectomy specimens. We demonstrate that tuning the regularization parameters results in significant improvements in accuracy and we also show that SIFTFlow outperforms a previously described edge-based registration method. The accuracy of the histology images to blockface images registration using the optimized SIFTFlow method was assessed using an independent test set of images from five different lumpectomy specimens and the mean registration error was 0.32±0.22 mm.
The aim of this paper is to validate an image registration pipeline used for histology image alignment. In this work a set
of histology images are registered to their correspondent optical blockface images to make a histology volume. Then
multi-modality fiducial markers are used to validate the alignment of histology images. The fiducial markers are
catheters perfused with a mixture of cuttlefish ink and flour. Based on our previous investigations this fiducial marker is
visible in medical images, optical blockface images and it can also be localized in histology images. The properties of
this fiducial marker make it suitable for validation of the registration techniques used for histology image alignment.
This paper reports on the accuracy of a histology image registration approach by calculation of target registration error
using these fiducial markers.
A multi-modality fiducial marker is presented in this work, which can be used for validating the correlation of histology
images with medical images. This marker can also be used for landmark-based image registration. Seven different
fiducial markers including a catheter, spaghetti, black spaghetti, cuttlefish ink, and liquid iron are implanted in a mouse
specimen and then investigated based on visibility, localization, size, and stability. The black spaghetti and the mixture
of cuttlefish ink and flour are shown to be the most suitable markers. Based on the size of the markers, black spaghetti is
more suitable for big specimens and the mixture of the cuttlefish ink, flour, and water injected in a catheter is more
suitable for small specimens such as mouse tumours. These markers are visible on medical images and also detectable on
histology and optical images of the tissue blocks. The main component in these agents which enhances the contrast is