Dataset Information

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

ABSTRACT: Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

SUBMITTER: Borovec J

PROVIDER: S-EPMC7584382 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

Borovec Jiri J Kybic Jan J Arganda-Carreras Ignacio I Sorokin Dmitry V DV Bueno Gloria G Khvostikov Alexander V AV Bakas Spyridon S Chang Eric I-Chao EI Heldmann Stefan S Kartasalo Kimmo K Latonen Leena L Lotz Johannes J Noga Michelle M Pati Sarthak S Punithakumar Kumaradevan K Ruusuvuori Pekka P Skalski Andrzej A Tahmasebi Nazanin N Valkonen Masi M Venet Ludovic L Wang Yizhe Y Weiss Nick N Wodzinski Marek M Xiang Yu Y Xu Yan Y Yan Yan Y Yushkevich Paul P Zhao Shengyu S Munoz-Barrutia Arrate A

IEEE transactions on medical imaging 20200407 10

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and ...[more]

PMID: 32275587

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Project description:Background[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) is a valuable tool for monitoring response to therapy in oncology. In longitudinal studies, however, patients are not scanned in exactly the same position. Rigid and non-rigid image registration can be applied in order to reuse baseline volumes of interest (VOI) on consecutive studies of the same patient. The purpose of this study was to investigate the impact of various image registration strategies on standardized uptake value (SUV) and metabolic volume test-retest variability (TRT).MethodsTest-retest whole-body [18F]FDG PET/CT scans were collected retrospectively for 11 subjects with advanced gastrointestinal malignancies (colorectal carcinoma). Rigid and non-rigid image registration techniques with various degrees of locality were applied to PET, CT, and non-attenuation corrected PET (NAC) data. VOI were drawn independently on both test and retest scans. VOI drawn on test scans were projected onto retest scans and the overlap between projected VOI and manually drawn retest VOI was quantified using the Dice similarity coefficient (DSC). In addition, absolute (unsigned) differences in TRT of SUVmax, SUVmean, metabolic volume and total lesion glycolysis (TLG) were calculated in on one hand the test VOI and on the other hand the retest VOI and projected VOI. Reference values were obtained by delineating VOIs on both scans separately.ResultsNon-rigid PET registration showed the best performance (median DSC: 0.82, other methods: 0.71-0.81). Compared with the reference, none of the registration types showed significant absolute differences in TRT of SUVmax, SUVmean and TLG (p > 0.05). Only for absolute TRT of metabolic volume, significant lower values (p < 0.05) were observed for all registration strategies when compared to delineating VOIs separately, except for non-rigid PET registrations (p = 0.1). Non-rigid PET registration provided good volume TRT (7.7%) that was smaller than the reference (16%).ConclusionIn particular, non-rigid PET image registration showed good performance similar to delineating VOI on both scans separately, and with smaller TRT in metabolic volume estimates.

Dataset Information

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

Publications

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

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