Multi-Modality fiducial marker for validation of registration of medical images with histology

Shojaii, Rushin; Martel, Anne L.

doi:10.1117/12.845551

Cited by 2 publications

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“…An edge-based registration method made use of points at the boundary between the tissue specimen and the surrounding paraffin block to carry out a rigid registration followed by a nonrigid deformation. We used implanted fiducial markers 27 to measure the registration accuracy in mouse xenograft specimens and found that the deformable registration generated using the boundary points actually degraded the accuracy of the global rigid registration. 26 This highlights the need to improve the deformable registration step in the pipeline.…”

Section: Related Workmentioning

confidence: 99%

“…Four multimodality fiducial markers were implanted in the fresh tissue using a biopsy needle. 27 The specimen was suspended in 3.5% agar gel and four additional fiducial markers were placed in the agar gel close to the specimen; these markers could not be used for the histology to blockface registration as the gel tended to become detached from the tissue during sectioning. The fresh tissue was sliced at 4 mm.…”

Section: Image Datamentioning

confidence: 99%

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Optimized SIFTFlow for registration of whole-mount histology to reference optical images

Shojaii

Martel

2016

J. Med. Imag

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Abstract. 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 AE 0.22 mm. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Related Workmentioning

confidence: 99%

Section: Image Datamentioning

confidence: 99%