2016
Rushin Shojaii, Anne L Martel
Optimized SIFTFlow for registration of whole-mount histology to reference optical images Journal Article
In: Journal of Medical Imaging, vol. 3, no. 4, pp. 047501-047501, 2016.
Abstract | Links | BibTeX | Tags: digital pathology, registration
@article{Shojaii2016,
title = {Optimized SIFTFlow for registration of whole-mount histology to reference optical images},
author = {Rushin Shojaii, Anne L Martel},
url = {http://medicalimaging.spiedigitallibrary.org/article.aspx?articleid=2571703},
doi = {10.1117/1.JMI.3.4.047501},
year = {2016},
date = {2016-10-19},
journal = {Journal of Medical Imaging},
volume = {3},
number = {4},
pages = {047501-047501},
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±0.22 mm0.32±0.22 mm.},
keywords = {digital pathology, registration},
pubstate = {published},
tppubtype = {article}
}
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 mm0.32±0.22 mm.
2011
Lausch, Anthony
Nonrigid Registration of Dynamic Contrast-enhanced MRI Data using Motion Informed Intensity Corrections Masters Thesis
University of Toronto, Medical Biophysics, 2011.
Abstract | Links | BibTeX | Tags: registration
@mastersthesis{Lausch2011,
title = {Nonrigid Registration of Dynamic Contrast-enhanced MRI Data using Motion Informed Intensity Corrections},
author = {Lausch, Anthony},
url = {http://hdl.handle.net/1807/31294},
year = {2011},
date = {2011-12-13},
address = {Medical Biophysics},
school = {University of Toronto},
abstract = {Effective early detection and monitoring of patient response to cancer therapy is important for improved patient outcomes, avoiding unnecessary procedures and their associated toxicities, as well as the development of new therapies. Dynamic contrast-enhanced magnetic resonance imaging shows promise as a way to evaluate tumour vasculature and assess the efficacy of new anti-angiogenic drugs. However, unavoidable patient motion can decrease the accuracy of subsequent analyses rendering the data unusable. Motion correction algorithms are challenging to develop for contrast-enhanced data since intensity changes due to contrast-enhancement and patient motion must somehow be differentiated from one another. A novel method is presented that employs a motion-informed intensity correction in order to facilitate the registration of contrast enhanced data. The intensity correction simulates the presence or absence of contrast agent in the image volumes to be registered in an attempt to emulate the level of contrast-enhancement present in a single reference image volume.},
keywords = {registration},
pubstate = {published},
tppubtype = {mastersthesis}
}
Effective early detection and monitoring of patient response to cancer therapy is important for improved patient outcomes, avoiding unnecessary procedures and their associated toxicities, as well as the development of new therapies. Dynamic contrast-enhanced magnetic resonance imaging shows promise as a way to evaluate tumour vasculature and assess the efficacy of new anti-angiogenic drugs. However, unavoidable patient motion can decrease the accuracy of subsequent analyses rendering the data unusable. Motion correction algorithms are challenging to develop for contrast-enhanced data since intensity changes due to contrast-enhancement and patient motion must somehow be differentiated from one another. A novel method is presented that employs a motion-informed intensity correction in order to facilitate the registration of contrast enhanced data. The intensity correction simulates the presence or absence of contrast agent in the image volumes to be registered in an attempt to emulate the level of contrast-enhancement present in a single reference image volume.