Dataset Open Access

Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data

Oleg Lobachev; Alexander Pfaffenroth; Christoph Wrede; David Haberthür; Ruslan Hlushchuk; Thomas Salaets; Jaan Toelen

Evaluating Registrations of Serial Sections With Distortions of the Ground Truths

This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows:

  • *_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.
  • *_local-only.7z: only local distortions applied.
  • *_local-DIST.7z: the distortion maps for local distortions.
  • *_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.
  • _*vis.7z: visualizations of distortions.
  • _rigid_ground.7z: the real rigid transformations used in the global phase.
  • *_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.

There are three main modalities and one further, as a reference:

  • CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)
  • EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)
  • LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)
  • REAL_*: a region from real serial sections from a rabbit lung, 2 images.

We also supply elastix parameter files.

A preprint has been uploaded to arXiv. The definite version is available from IEEE. The source code of the distorter is available from GitHub.

This work was supported by DFG grant MU 3118/8-1. This work was partially supported by JST, PRESTO grant number JPMJPR2025, Japan. This research was supported by a C2 grant from KU Leuven (C24/18/101) and a research grant from the Research Foundation – Flanders (FWO G0C4419N). None of the funding bodies was involved in the design or execution of the study.
Files (14.2 GB)
Name Size
CT_challenge.7z
md5:c3c2830c5c6b5a1651bf95b4130706ad
1.7 GB Download
CT_ground.7z
md5:938b97da7889db58e54aa46d830abae5
1.9 GB Download
CT_local-DIST.7z
md5:716e9ddecf572b4f6c6a7f47e2bc296b
117.9 MB Download
CT_local-only.7z
md5:16744be68955edcbb834e06eb18428e7
2.2 GB Download
CT_rigid_ground.7z
md5:f8eb80522f83089f4a8a7c27ef6e2e49
46.0 kB Download
CT_SURF-rigid.7z
md5:4a7882593b3f22dfd3ee41d7e3ac3cde
1.9 GB Download
elastix_params.7z
md5:1cccd9ad4d62c147ac943ce700b48eee
2.4 kB Download
EM_challenge.7z
md5:d62a4eeea36e01d6a778556982dd7947
258.9 MB Download
EM_ground-norm.7z
md5:f44441564a3fb924ec49de0f7babf7bb
211.1 MB Download
EM_local-DIST.7z
md5:516869896dea245bc7c9eec9a74f18d3
58.5 MB Download
EM_local-only.7z
md5:b40b6608f1a8515228ca28c9dd294bb6
246.3 MB Download
EM_rigid_ground.7z
md5:687cce6c28aaaedcb4ec025bc60aec2a
67.2 kB Download
EM_SURF-rigid.7z
md5:dbe9722c636b79772ca339098f7a0fbf
282.3 MB Download
LS_challenge.7z
md5:57fb93272736b5417e6ca86f2bcf4d44
739.0 MB Download
LS_global-TRANS.7z
md5:1a744f1e49f14b4a33607c29110518b7
21.4 kB Download
LS_ground.7z
md5:a5387e7580423200cd6d5af84249f76a
916.4 MB Download
LS_local-DIST.7z
md5:35de9daa63a706b4d6cb017e7ca9d623
51.6 MB Download
LS_local-only.7z
md5:db521decb680227313eea1fa8437939e
991.3 MB Download
LS_rigid_ground.7z
md5:2f87518260cd832aa18f5a8738779e51
21.5 kB Download
LS_SURF-rigid.7z
md5:73986ff320bcd34499cf1935d2cbaf6c
586.2 MB Download
LS_vis.7z
md5:e397ca3878207cc4f1558fe78db5dc94
622.9 MB Download
REAL_challenge.7z
md5:35ec26a22842898f13346f6320005d06
1.4 GB Download
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