TY - GEN
T1 - Automatic Multi-Stain Registration of Whole Slide Images in Histopathology
AU - Shafique, Abubakr
AU - Babaie, Morteza
AU - Sajadi, Mahjabin
AU - Batten, Adrian
AU - Skdar, Soma
AU - Tizhoosh, H. R.
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immune-histochemical and hematoxylin and eosin (HE) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.
AB - Joint analysis of multiple biomarker images and tissue morphology is important for disease diagnosis, treatment planning and drug development. It requires cross-staining comparison among Whole Slide Images (WSIs) of immune-histochemical and hematoxylin and eosin (HE) microscopic slides. However, automatic, and fast cross-staining alignment of enormous gigapixel WSIs at single-cell precision is challenging. In addition to morphological deformations introduced during slide preparation, there are large variations in cell appearance and tissue morphology across different staining. In this paper, we propose a two-step automatic feature-based cross-staining WSI alignment to assist localization of even tiny metastatic foci in the assessment of lymph node. Image pairs were aligned allowing for translation, rotation, and scaling. The registration was performed automatically by first detecting landmarks in both images, using the scale-invariant image transform (SIFT), followed by the fast sample consensus (FSC) protocol for finding point correspondences and finally aligned the images. The Registration results were evaluated using both visual and quantitative criteria using the Jaccard index. The average Jaccard similarity index of the results produced by the proposed system is 0.942 when compared with the manual registration.
KW - image alignment
KW - image registration
KW - immunohistochemistry
KW - multi-stain
KW - pathology
UR - http://www.scopus.com/inward/record.url?scp=85122495557&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85122495557&partnerID=8YFLogxK
U2 - 10.1109/EMBC46164.2021.9629970
DO - 10.1109/EMBC46164.2021.9629970
M3 - Conference contribution
C2 - 34892022
AN - SCOPUS:85122495557
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3622
EP - 3625
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -