supplements

suppl/0_renaming.md

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+# Renaming files
+The filenames of the images in the original datasets are complex or not unique. Thus, the following steps were performed:
+
+## TotalSegmentator
+The TotalSegmentator images are all named `ct.nii.gz`, and must be renamed. To do so, you can run the following bash script **within in the downloaded directory** (e.g., `Totalsegmentator_dataset_v201`). Remove `echo` only after double checking.
+
+```bash
+for dir in */; do 
+  echo mv -v "${dir}ct.nii.gz" "../tseg-${dir::-1}.nii.gz"
+done
+```
+
+> In this dataset, files derived from CT scans in the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator/) dataset are prefixed with `tseg-` followed by the corresponding TotalSegmentator ID (e.g., `tseg-s1317.nii.gz`).
+
+## VerSe
+The VerSe images file names include descriptions of the scan, orientation and mask. To make the filenames more simple and uniform, we removed the unnecessary suffixes. To do so too, you can run the following bash commands **within in the directory containing all downloaded NIfTI files**. Remove `echo` only after double checking!
+
+```bash
+for f in *.nii.gz; do echo mv "$f" "${f/split-/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/sub-/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/_dir/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/_ct/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/-ax/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/-iso/}"; done
+for f in *.nii.gz; do echo mv "$f" "${f/-sag/}"; done
+
+for file in *.nii.gz ; do
+    echo mv -v "$file" "verse-${file}"
+done
+```
+
+> In this dataset, files derived from CT scans in the [VerSe](https://github.com/anjany/verse) dataset are prefixed with `verse-` followed by the corresponding VerSe ID (e.g., `verse-825.nii.gz`).

suppl/1_exclusions.md

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+# Exclusions
+
+Due to non-orthogonal direction matrices or missing slices, 142 patients were exluded from further labeling. The final dataset consists of 1460 computed tomography images and associated labels of the vertebral bodies, 1088 from the [TotalSegmentator](https://github.com/wasserth/TotalSegmentator/) dataset and 372 from the [VerSe](https://github.com/anjany/verse) dataset.
+
+## Non-Orthogonal Direction Matrix
+
+In 140 cases, the direction matrix was non-orthogonal and the ITK tool (e.g. used by [nnU-Net](https://github.com/MIC-DKFZ/nnUNet)) had problems in opening the images. The cases were identified by [others](https://github.com/wasserth/TotalSegmentator/issues/268) and through own analyses. Thus, these imaging studies were excluded.
+
+``` python
+exclusion_non_orthogonal = ['tseg-s0000.nii.gz', 'tseg-s0001.nii.gz', 'tseg-s0002.nii.gz', 'tseg-s0004.nii.gz', 'tseg-s0025.nii.gz', 'tseg-s0043.nii.gz', 'tseg-s0048.nii.gz', 'tseg-s0061.nii.gz', 'tseg-s0062.nii.gz', 'tseg-s0066.nii.gz', 'tseg-s0067.nii.gz', 'tseg-s0079.nii.gz', 'tseg-s0081.nii.gz', 'tseg-s0090.nii.gz', 'tseg-s0107.nii.gz', 'tseg-s0112.nii.gz', 'tseg-s0114.nii.gz', 'tseg-s0117.nii.gz', 'tseg-s0130.nii.gz', 'tseg-s0146.nii.gz', 'tseg-s0149.nii.gz', 'tseg-s0154.nii.gz', 'tseg-s0169.nii.gz', 'tseg-s0189.nii.gz', 'tseg-s0193.nii.gz', 'tseg-s0208.nii.gz', 'tseg-s0209.nii.gz', 'tseg-s0226.nii.gz', 'tseg-s0235.nii.gz', 'tseg-s0236.nii.gz', 'tseg-s0242.nii.gz', 'tseg-s0254.nii.gz', 'tseg-s0278.nii.gz', 'tseg-s0282.nii.gz', 'tseg-s0295.nii.gz', 'tseg-s0299.nii.gz', 'tseg-s0301.nii.gz', 'tseg-s0311.nii.gz', 'tseg-s0326.nii.gz', 'tseg-s0352.nii.gz', 'tseg-s0353.nii.gz', 'tseg-s0361.nii.gz', 'tseg-s0386.nii.gz', 'tseg-s0396.nii.gz', 'tseg-s0417.nii.gz', 'tseg-s0436.nii.gz', 'tseg-s0457.nii.gz', 'tseg-s0462.nii.gz', 'tseg-s0493.nii.gz', 'tseg-s0504.nii.gz', 'tseg-s0514.nii.gz', 'tseg-s0526.nii.gz', 'tseg-s0527.nii.gz', 'tseg-s0552.nii.gz', 'tseg-s0557.nii.gz', 'tseg-s0559.nii.gz', 'tseg-s0561.nii.gz', 'tseg-s0563.nii.gz', 'tseg-s0585.nii.gz', 'tseg-s0611.nii.gz', 'tseg-s0614.nii.gz', 'tseg-s0622.nii.gz', 'tseg-s0624.nii.gz', 'tseg-s0625.nii.gz', 'tseg-s0647.nii.gz', 'tseg-s0648.nii.gz', 'tseg-s0684.nii.gz', 'tseg-s0700.nii.gz', 'tseg-s0712.nii.gz', 'tseg-s0735.nii.gz', 'tseg-s0737.nii.gz', 'tseg-s0740.nii.gz', 'tseg-s0744.nii.gz', 'tseg-s0751.nii.gz', 'tseg-s0759.nii.gz', 'tseg-s0768.nii.gz', 'tseg-s0770.nii.gz', 'tseg-s0772.nii.gz', 'tseg-s0774.nii.gz', 'tseg-s0776.nii.gz', 'tseg-s0783.nii.gz', 'tseg-s0795.nii.gz', 'tseg-s0802.nii.gz', 'tseg-s0805.nii.gz', 'tseg-s0806.nii.gz', 'tseg-s0808.nii.gz', 'tseg-s0809.nii.gz', 'tseg-s0816.nii.gz', 'tseg-s0829.nii.gz', 'tseg-s0833.nii.gz', 'tseg-s0837.nii.gz', 'tseg-s0843.nii.gz', 'tseg-s0844.nii.gz', 'tseg-s0845.nii.gz', 'tseg-s0860.nii.gz', 'tseg-s0867.nii.gz', 'tseg-s0874.nii.gz', 'tseg-s0909.nii.gz', 'tseg-s0910.nii.gz', 'tseg-s0930.nii.gz', 'tseg-s0944.nii.gz', 'tseg-s0949.nii.gz', 'tseg-s0976.nii.gz', 'tseg-s0978.nii.gz', 'tseg-s0982.nii.gz', 'tseg-s0986.nii.gz', 'tseg-s1000.nii.gz', 'tseg-s1005.nii.gz', 'tseg-s1009.nii.gz', 'tseg-s1011.nii.gz', 'tseg-s1023.nii.gz', 'tseg-s1035.nii.gz', 'tseg-s1038.nii.gz', 'tseg-s1040.nii.gz', 'tseg-s1046.nii.gz', 'tseg-s1050.nii.gz', 'tseg-s1083.nii.gz', 'tseg-s1103.nii.gz', 'tseg-s1133.nii.gz', 'tseg-s1141.nii.gz', 'tseg-s1147.nii.gz', 'tseg-s1148.nii.gz', 'tseg-s1170.nii.gz', 'tseg-s1174.nii.gz', 'tseg-s1205.nii.gz', 'tseg-s1234.nii.gz', 'tseg-s1235.nii.gz', 'tseg-s1237.nii.gz', 'tseg-s1272.nii.gz', 'tseg-s1276.nii.gz', 'tseg-s1295.nii.gz', 'tseg-s1344.nii.gz', 'tseg-s1389.nii.gz', 'tseg-s1406.nii.gz', 'tseg-s1407.nii.gz', 'tseg-s1409.nii.gz', 'tseg-s1417.nii.gz', 'tseg-s1419.nii.gz', 'verse-700.nii.gz', 'verse-813.nii.gz']
+```
+
+## Missing slices
+In two patients from the TotalSegmentator dataset, images had missing slices. Thus, these patients were excluded from both, the training and the validation dataset.
+
+``` python
+exclusion_missing_slices = ['tseg-s1018.nii.gz', 'tseg-s1063.nii.gz']
+```

suppl/2_errors.md

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+# Erroneous Spine Labels
+
+Some spine labels of the TotalSegmentator dataset were found to contain minor or relevant inaccuracies, as identified by [others](https://github.com/wasserth/TotalSegmentator/issues/268) or through our own analyses. These cases were temporarily excluded from the training of a preliminary nnU-Net model, which was used to (pseudo-)label the errorenous cases in the TotalSegmentator dataset and to adress the missing vertebral body labels in the VerSe dataset. For details of the multi-stage labeling process, please refer to our paper.
+
+``` python
+spine_errors = ['tseg-s0009.nii.gz', 'tseg-s0011.nii.gz', 'tseg-s0016.nii.gz', 'tseg-s0028.nii.gz', 'tseg-s0029.nii.gz', 'tseg-s0049.nii.gz', 'tseg-s0059.nii.gz', 'tseg-s0068.nii.gz', 'tseg-s0070.nii.gz', 'tseg-s0071.nii.gz', 'tseg-s0076.nii.gz', 'tseg-s0077.nii.gz', 'tseg-s0083.nii.gz', 'tseg-s0084.nii.gz', 'tseg-s0086.nii.gz', 'tseg-s0089.nii.gz', 'tseg-s0095.nii.gz', 'tseg-s0096.nii.gz', 'tseg-s0098.nii.gz', 'tseg-s0099.nii.gz', 'tseg-s0104.nii.gz', 'tseg-s0108.nii.gz', 'tseg-s0128.nii.gz', 'tseg-s0133.nii.gz', 'tseg-s0135.nii.gz', 'tseg-s0136.nii.gz', 'tseg-s0140.nii.gz', 'tseg-s0145.nii.gz', 'tseg-s0158.nii.gz', 'tseg-s0161.nii.gz', 'tseg-s0167.nii.gz', 'tseg-s0171.nii.gz', 'tseg-s0181.nii.gz', 'tseg-s0184.nii.gz', 'tseg-s0192.nii.gz', 'tseg-s0196.nii.gz', 'tseg-s0206.nii.gz', 'tseg-s0210.nii.gz', 'tseg-s0216.nii.gz', 'tseg-s0219.nii.gz', 'tseg-s0223.nii.gz', 'tseg-s0230.nii.gz', 'tseg-s0234.nii.gz', 'tseg-s0238.nii.gz', 'tseg-s0239.nii.gz', 'tseg-s0240.nii.gz', 'tseg-s0257.nii.gz', 'tseg-s0259.nii.gz', 'tseg-s0271.nii.gz', 'tseg-s0281.nii.gz', 'tseg-s0283.nii.gz', 'tseg-s0296.nii.gz', 'tseg-s0298.nii.gz', 'tseg-s0303.nii.gz', 'tseg-s0305.nii.gz', 'tseg-s0315.nii.gz', 'tseg-s0331.nii.gz', 'tseg-s0335.nii.gz', 'tseg-s0344.nii.gz', 'tseg-s0345.nii.gz', 'tseg-s0350.nii.gz', 'tseg-s0357.nii.gz', 'tseg-s0358.nii.gz', 'tseg-s0362.nii.gz', 'tseg-s0366.nii.gz', 'tseg-s0368.nii.gz', 'tseg-s0369.nii.gz', 'tseg-s0377.nii.gz', 'tseg-s0378.nii.gz', 'tseg-s0379.nii.gz', 'tseg-s0382.nii.gz', 'tseg-s0385.nii.gz', 'tseg-s0389.nii.gz', 'tseg-s0395.nii.gz', 'tseg-s0396.nii.gz', 'tseg-s0399.nii.gz', 'tseg-s0400.nii.gz', 'tseg-s0407.nii.gz', 'tseg-s0408.nii.gz', 'tseg-s0410.nii.gz', 'tseg-s0411.nii.gz', 'tseg-s0414.nii.gz', 'tseg-s0419.nii.gz', 'tseg-s0422.nii.gz', 'tseg-s0428.nii.gz', 'tseg-s0437.nii.gz', 'tseg-s0439.nii.gz', 'tseg-s0442.nii.gz', 'tseg-s0446.nii.gz', 'tseg-s0452.nii.gz', 'tseg-s0453.nii.gz', 'tseg-s0454.nii.gz', 'tseg-s0456.nii.gz', 'tseg-s0460.nii.gz', 'tseg-s0461.nii.gz', 'tseg-s0465.nii.gz', 'tseg-s0474.nii.gz', 'tseg-s0481.nii.gz', 'tseg-s0488.nii.gz', 'tseg-s0491.nii.gz', 'tseg-s0492.nii.gz', 'tseg-s0493.nii.gz', 'tseg-s0508.nii.gz', 'tseg-s0510.nii.gz', 'tseg-s0511.nii.gz', 'tseg-s0513.nii.gz', 'tseg-s0515.nii.gz', 'tseg-s0517.nii.gz', 'tseg-s0519.nii.gz', 'tseg-s0520.nii.gz', 'tseg-s0521.nii.gz', 'tseg-s0522.nii.gz', 'tseg-s0525.nii.gz', 'tseg-s0529.nii.gz', 'tseg-s0531.nii.gz', 'tseg-s0532.nii.gz', 'tseg-s0535.nii.gz', 'tseg-s0536.nii.gz', 'tseg-s0543.nii.gz', 'tseg-s0544.nii.gz', 'tseg-s0546.nii.gz', 'tseg-s0549.nii.gz', 'tseg-s0551.nii.gz', 'tseg-s0555.nii.gz', 'tseg-s0564.nii.gz', 'tseg-s0567.nii.gz', 'tseg-s0568.nii.gz', 'tseg-s0572.nii.gz', 'tseg-s0573.nii.gz', 'tseg-s0575.nii.gz', 'tseg-s0580.nii.gz', 'tseg-s0585.nii.gz', 'tseg-s0591.nii.gz', 'tseg-s0597.nii.gz', 'tseg-s0613.nii.gz', 'tseg-s0615.nii.gz', 'tseg-s0616.nii.gz', 'tseg-s0618.nii.gz', 'tseg-s0619.nii.gz', 'tseg-s0622.nii.gz', 'tseg-s0624.nii.gz', 'tseg-s0627.nii.gz', 'tseg-s0629.nii.gz', 'tseg-s0632.nii.gz', 'tseg-s0636.nii.gz', 'tseg-s0641.nii.gz', 'tseg-s0642.nii.gz', 'tseg-s0644.nii.gz', 'tseg-s0649.nii.gz', 'tseg-s0653.nii.gz', 'tseg-s0654.nii.gz', 'tseg-s0655.nii.gz', 'tseg-s0656.nii.gz', 'tseg-s0658.nii.gz', 'tseg-s0659.nii.gz', 'tseg-s0660.nii.gz', 'tseg-s0661.nii.gz', 'tseg-s0664.nii.gz', 'tseg-s0669.nii.gz', 'tseg-s0670.nii.gz', 'tseg-s0671.nii.gz', 'tseg-s0674.nii.gz', 'tseg-s0676.nii.gz', 'tseg-s0679.nii.gz', 'tseg-s0680.nii.gz', 'tseg-s0684.nii.gz', 'tseg-s0686.nii.gz', 'tseg-s0687.nii.gz', 'tseg-s0688.nii.gz', 'tseg-s0691.nii.gz', 'tseg-s0692.nii.gz', 'tseg-s0693.nii.gz', 'tseg-s0695.nii.gz', 'tseg-s0698.nii.gz', 'tseg-s0699.nii.gz', 'tseg-s0700.nii.gz', 'tseg-s0703.nii.gz', 'tseg-s0709.nii.gz', 'tseg-s0710.nii.gz', 'tseg-s0714.nii.gz', 'tseg-s0716.nii.gz', 'tseg-s0717.nii.gz', 'tseg-s0718.nii.gz', 'tseg-s0720.nii.gz', 'tseg-s0723.nii.gz', 'tseg-s0726.nii.gz', 'tseg-s0727.nii.gz', 'tseg-s0728.nii.gz', 'tseg-s0731.nii.gz', 'tseg-s0732.nii.gz', 'tseg-s0733.nii.gz', 'tseg-s0740.nii.gz', 'tseg-s0741.nii.gz', 'tseg-s0743.nii.gz', 'tseg-s0744.nii.gz', 'tseg-s0747.nii.gz', 'tseg-s0748.nii.gz', 'tseg-s0749.nii.gz', 'tseg-s0753.nii.gz', 'tseg-s0754.nii.gz', 'tseg-s0763.nii.gz', 'tseg-s0765.nii.gz', 'tseg-s0766.nii.gz', 'tseg-s0767.nii.gz', 'tseg-s0771.nii.gz', 'tseg-s0776.nii.gz', 'tseg-s0777.nii.gz', 'tseg-s0781.nii.gz', 'tseg-s0782.nii.gz', 'tseg-s0785.nii.gz', 'tseg-s0788.nii.gz', 'tseg-s0789.nii.gz', 'tseg-s0790.nii.gz', 'tseg-s0792.nii.gz', 'tseg-s0793.nii.gz', 'tseg-s0796.nii.gz', 'tseg-s0797.nii.gz', 'tseg-s0800.nii.gz', 'tseg-s0802.nii.gz', 'tseg-s0805.nii.gz', 'tseg-s0806.nii.gz', 'tseg-s0807.nii.gz', 'tseg-s0808.nii.gz', 'tseg-s0812.nii.gz', 'tseg-s0813.nii.gz', 'tseg-s0815.nii.gz', 'tseg-s0816.nii.gz', 'tseg-s0819.nii.gz', 'tseg-s0820.nii.gz', 'tseg-s0822.nii.gz', 'tseg-s0824.nii.gz', 'tseg-s0826.nii.gz', 'tseg-s0829.nii.gz', 'tseg-s0831.nii.gz', 'tseg-s0834.nii.gz', 'tseg-s0840.nii.gz', 'tseg-s0843.nii.gz', 'tseg-s0847.nii.gz', 'tseg-s0852.nii.gz', 'tseg-s0853.nii.gz', 'tseg-s0857.nii.gz', 'tseg-s0859.nii.gz', 'tseg-s0861.nii.gz', 'tseg-s0863.nii.gz', 'tseg-s0866.nii.gz', 'tseg-s0867.nii.gz', 'tseg-s0870.nii.gz', 'tseg-s0874.nii.gz', 'tseg-s0877.nii.gz', 'tseg-s0878.nii.gz', 'tseg-s0879.nii.gz', 'tseg-s0880.nii.gz', 'tseg-s0885.nii.gz', 'tseg-s0889.nii.gz', 'tseg-s0890.nii.gz', 'tseg-s0891.nii.gz', 'tseg-s0892.nii.gz', 'tseg-s0896.nii.gz', 'tseg-s0899.nii.gz', 'tseg-s0903.nii.gz', 'tseg-s0908.nii.gz', 'tseg-s0909.nii.gz', 'tseg-s0910.nii.gz', 'tseg-s0918.nii.gz', 'tseg-s0922.nii.gz', 'tseg-s0923.nii.gz', 'tseg-s0927.nii.gz', 'tseg-s0928.nii.gz', 'tseg-s0931.nii.gz', 'tseg-s0933.nii.gz', 'tseg-s0934.nii.gz', 'tseg-s0935.nii.gz', 'tseg-s0936.nii.gz', 'tseg-s0938.nii.gz', 'tseg-s0939.nii.gz', 'tseg-s0940.nii.gz', 'tseg-s0941.nii.gz', 'tseg-s0946.nii.gz', 'tseg-s0950.nii.gz', 'tseg-s0952.nii.gz', 'tseg-s0953.nii.gz', 'tseg-s0954.nii.gz', 'tseg-s0956.nii.gz', 'tseg-s0957.nii.gz', 'tseg-s0958.nii.gz', 'tseg-s0962.nii.gz', 'tseg-s0963.nii.gz', 'tseg-s0965.nii.gz', 'tseg-s0968.nii.gz', 'tseg-s0970.nii.gz', 'tseg-s0972.nii.gz', 'tseg-s0974.nii.gz', 'tseg-s0980.nii.gz', 'tseg-s0984.nii.gz', 'tseg-s0987.nii.gz', 'tseg-s0991.nii.gz', 'tseg-s0993.nii.gz', 'tseg-s0994.nii.gz', 'tseg-s0995.nii.gz', 'tseg-s0996.nii.gz', 'tseg-s0997.nii.gz', 'tseg-s1001.nii.gz', 'tseg-s1006.nii.gz', 'tseg-s1010.nii.gz', 'tseg-s1013.nii.gz', 'tseg-s1014.nii.gz', 'tseg-s1016.nii.gz', 'tseg-s1017.nii.gz', 'tseg-s1018.nii.gz', 'tseg-s1020.nii.gz', 'tseg-s1022.nii.gz', 'tseg-s1024.nii.gz', 'tseg-s1025.nii.gz', 'tseg-s1026.nii.gz', 'tseg-s1029.nii.gz', 'tseg-s1031.nii.gz', 'tseg-s1033.nii.gz', 'tseg-s1036.nii.gz', 'tseg-s1039.nii.gz', 'tseg-s1041.nii.gz', 'tseg-s1042.nii.gz', 'tseg-s1043.nii.gz', 'tseg-s1044.nii.gz', 'tseg-s1045.nii.gz', 'tseg-s1047.nii.gz', 'tseg-s1048.nii.gz', 'tseg-s1049.nii.gz', 'tseg-s1051.nii.gz', 'tseg-s1055.nii.gz', 'tseg-s1060.nii.gz', 'tseg-s1062.nii.gz', 'tseg-s1063.nii.gz', 'tseg-s1065.nii.gz', 'tseg-s1066.nii.gz', 'tseg-s1071.nii.gz', 'tseg-s1072.nii.gz', 'tseg-s1075.nii.gz', 'tseg-s1077.nii.gz', 'tseg-s1082.nii.gz', 'tseg-s1088.nii.gz', 'tseg-s1089.nii.gz', 'tseg-s1090.nii.gz', 'tseg-s1094.nii.gz', 'tseg-s1097.nii.gz', 'tseg-s1101.nii.gz', 'tseg-s1102.nii.gz', 'tseg-s1105.nii.gz', 'tseg-s1109.nii.gz', 'tseg-s1110.nii.gz', 'tseg-s1111.nii.gz', 'tseg-s1112.nii.gz', 'tseg-s1113.nii.gz', 'tseg-s1114.nii.gz', 'tseg-s1115.nii.gz', 'tseg-s1119.nii.gz', 'tseg-s1120.nii.gz', 'tseg-s1121.nii.gz', 'tseg-s1123.nii.gz', 'tseg-s1124.nii.gz', 'tseg-s1127.nii.gz', 'tseg-s1128.nii.gz', 'tseg-s1132.nii.gz', 'tseg-s1138.nii.gz', 'tseg-s1140.nii.gz', 'tseg-s1143.nii.gz', 'tseg-s1144.nii.gz', 'tseg-s1148.nii.gz', 'tseg-s1149.nii.gz', 'tseg-s1150.nii.gz', 'tseg-s1154.nii.gz', 'tseg-s1155.nii.gz', 'tseg-s1159.nii.gz', 'tseg-s1162.nii.gz', 'tseg-s1164.nii.gz', 'tseg-s1165.nii.gz', 'tseg-s1167.nii.gz', 'tseg-s1168.nii.gz', 'tseg-s1169.nii.gz', 'tseg-s1171.nii.gz', 'tseg-s1173.nii.gz', 'tseg-s1175.nii.gz', 'tseg-s1178.nii.gz', 'tseg-s1179.nii.gz', 'tseg-s1184.nii.gz', 'tseg-s1189.nii.gz', 'tseg-s1192.nii.gz', 'tseg-s1199.nii.gz', 'tseg-s1201.nii.gz', 'tseg-s1203.nii.gz', 'tseg-s1206.nii.gz', 'tseg-s1208.nii.gz', 'tseg-s1209.nii.gz', 'tseg-s1210.nii.gz', 'tseg-s1212.nii.gz', 'tseg-s1216.nii.gz', 'tseg-s1220.nii.gz', 'tseg-s1221.nii.gz', 'tseg-s1222.nii.gz', 'tseg-s1223.nii.gz', 'tseg-s1224.nii.gz', 'tseg-s1226.nii.gz', 'tseg-s1230.nii.gz', 'tseg-s1231.nii.gz', 'tseg-s1233.nii.gz', 'tseg-s1238.nii.gz', 'tseg-s1240.nii.gz', 'tseg-s1241.nii.gz', 'tseg-s1244.nii.gz', 'tseg-s1245.nii.gz', 'tseg-s1246.nii.gz', 'tseg-s1247.nii.gz', 'tseg-s1248.nii.gz', 'tseg-s1249.nii.gz', 'tseg-s1250.nii.gz', 'tseg-s1251.nii.gz', 'tseg-s1258.nii.gz', 'tseg-s1259.nii.gz', 'tseg-s1260.nii.gz', 'tseg-s1264.nii.gz', 'tseg-s1267.nii.gz', 'tseg-s1268.nii.gz', 'tseg-s1269.nii.gz', 'tseg-s1270.nii.gz', 'tseg-s1274.nii.gz', 'tseg-s1275.nii.gz', 'tseg-s1277.nii.gz', 'tseg-s1280.nii.gz', 'tseg-s1281.nii.gz', 'tseg-s1283.nii.gz', 'tseg-s1285.nii.gz', 'tseg-s1287.nii.gz', 'tseg-s1289.nii.gz', 'tseg-s1290.nii.gz', 'tseg-s1294.nii.gz', 'tseg-s1297.nii.gz', 'tseg-s1301.nii.gz', 'tseg-s1304.nii.gz', 'tseg-s1305.nii.gz', 'tseg-s1307.nii.gz', 'tseg-s1311.nii.gz', 'tseg-s1312.nii.gz', 'tseg-s1314.nii.gz', 'tseg-s1315.nii.gz', 'tseg-s1317.nii.gz', 'tseg-s1319.nii.gz', 'tseg-s1323.nii.gz', 'tseg-s1325.nii.gz', 'tseg-s1326.nii.gz', 'tseg-s1327.nii.gz', 'tseg-s1329.nii.gz', 'tseg-s1330.nii.gz', 'tseg-s1336.nii.gz', 'tseg-s1340.nii.gz', 'tseg-s1341.nii.gz', 'tseg-s1342.nii.gz', 'tseg-s1343.nii.gz', 'tseg-s1345.nii.gz', 'tseg-s1346.nii.gz', 'tseg-s1348.nii.gz', 'tseg-s1349.nii.gz', 'tseg-s1350.nii.gz', 'tseg-s1353.nii.gz', 'tseg-s1354.nii.gz', 'tseg-s1357.nii.gz', 'tseg-s1361.nii.gz', 'tseg-s1362.nii.gz', 'tseg-s1363.nii.gz', 'tseg-s1364.nii.gz', 'tseg-s1365.nii.gz', 'tseg-s1366.nii.gz', 'tseg-s1367.nii.gz', 'tseg-s1369.nii.gz', 'tseg-s1370.nii.gz', 'tseg-s1371.nii.gz', 'tseg-s1372.nii.gz', 'tseg-s1375.nii.gz', 'tseg-s1379.nii.gz', 'tseg-s1382.nii.gz', 'tseg-s1387.nii.gz', 'tseg-s1388.nii.gz', 'tseg-s1390.nii.gz', 'tseg-s1391.nii.gz', 'tseg-s1395.nii.gz', 'tseg-s1397.nii.gz', 'tseg-s1399.nii.gz', 'tseg-s1404.nii.gz', 'tseg-s1405.nii.gz', 'tseg-s1408.nii.gz', 'tseg-s1410.nii.gz', 'tseg-s1412.nii.gz', 'tseg-s1414.nii.gz', 'tseg-s1415.nii.gz', 'tseg-s1416.nii.gz', 'tseg-s1418.nii.gz', 'tseg-s1423.nii.gz']
+```

suppl/3_vertebralbodies.md

@@ -0,0 +1,11 @@
+# Truncation to Vertebral Bodies
+
+To exclude the vertebral arch and spinous processes from the vertebrae's segmentation, we performed these steps:
+- **Spinal Cord Label:** Combined labels originating from or generated by [TotalSegmentator](https://github.com/wasserth/TotalSegmentator/) and generated by [Body-and-Organ-Analysis](https://github.com/UMEssen/Body-and-Organ-Analysis) to create a single label for the spinal cord.
+- **Label Reduction:** Limited vertebral labels to the voxels located anterior to the spinal cord label.
+- **3D Area Isolation:** Retained only the largest 3D-connected area for each label, removing extraneous voxels.
+- **Hole Filling:** Filled any holes (unassigned voxels completely surrounded by voxels of a single label).
+- **Smoothing:** Applied a smoothing algorithm to refine the edges of the labels.
+  
+**The script used for this process can be found in [4_MaskEdits.py](4_MaskEdits.py).**
+The resulting labels were then manually reviewed and corrected as needed.

suppl/4_MaskEdits.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python
+from pathlib import Path
+import argparse
+import nibabel as nib
+import numpy as np
+import os
+import multiprocessing
+from time import time
+import logging
+import traceback
+from scipy.ndimage import label as ndi_label, sum as ndi_sum
+from nibabel.orientations import io_orientation, axcodes2ornt, ornt_transform
+from scipy.ndimage import label as ndi_label, sum as ndi_sum, gaussian_filter
+
+# logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(levelname)s - %(message)s')
+
+# maping of input labels to output labels
+input_labels_map = {
+    "spine_to_vb": {
+        "labels-spine": {1:0,2:0,3:0,4:0,5:0,6:0,7:0, # removing cervical spine
+                         8:1,9:2,10:3,11:4,12:5,13:6,14:7,15:8,16:9,17:10,18:11,19:12,
+                         20:13,21:14,22:15,23:16,24:17,25:18,
+                         26:19,27:20,28:21,},
+        "labels-bodyregions": {11:1}, # = nervous_system 11
+        "labels-spinalcord":{1:1,79:1} # = spinal_cord, 1 for Tseg binary masks OR 79 for inference results; do not combine with task total (includes 1=spleen)
+    },
+}
+
+# definition of classes
+class ProcessLoader:
+    def __init__(self, input_root, method):
+        self.root = input_root
+        self.method = method
+        self.labels_map = input_labels_map[method]
+        logging.info(f"Initializing method `{method}`, loading relevant label map")
+
+    # reducing spine to vertebral bodies
+    def spine_to_vb(self, case):
+        output_np = case.fetch_label('labels-spine') # load original spine
+        myelon_np = case.fetch_label('labels-bodyregions') # load myelon from Body-and-Organ-Analysis, label nervous_system = 11
+        myelon2_np = case.fetch_label('labels-spinalcord') # load myelon from TotalSegmentator, label spinal_cord = 1 (for Tseg binary masks) OR 79 (for Inference)
+
+        # combine Body-and-Organ-Analysis and TotalSegmentator
+        myelon_np[myelon2_np > 0] = 1
+
+        # save sacrum temporarily, will not remove anything there
+        sacrum_np = output_np==19
+
+        # remove everything posterior from most anterior voxel of myelon
+        for slice in range(myelon_np.shape[2]):
+            slice_np = myelon_np[:,:,slice]
+            if np.sum(slice_np) > 0:
+                com_max = np.max(np.where(slice_np > 0), axis=1)[1] # most anterior
+                output_np[:,:com_max,slice] = 0
+
+        output_np[sacrum_np] = 19
+
+        # keep only the biggest connected component per vertebra (3D), removes noise
+        labels = np.unique(output_np)[1:]
+        for label in labels:
+            mask = output_np == label
+            if np.any(mask):
+                labeled, num_labels = ndi_label(mask)
+                sizes = ndi_sum(mask, labeled, index=range(1, num_labels+1))
+                largest_label = np.argmax(sizes) + 1
+                output_np[(labeled != largest_label) & mask] = 0
+
+        # fill holes in the vertebra mask
+        for label in labels:
+            mask = output_np == label
+            if np.any(mask):
+                np.invert(mask, out=mask)
+                labeled, num_labels = ndi_label(mask)
+                if num_labels <= 1:
+                    continue
+                sizes = ndi_sum(mask, labeled, index=range(1, num_labels+1))
+                largest_label = np.argmax(sizes) + 1
+                mask[labeled != largest_label] = 0
+                np.invert(mask, out=mask)
+                output_np[mask] = label
+
+        # smooth mask
+        output_np_smoothed = np.zeros_like(output_np)
+        for label in labels:
+            mask = output_np == label
+            if np.any(mask):
+                smoothed_mask = gaussian_filter(mask.astype(float), sigma=1) > 0.5
+                output_np_smoothed[smoothed_mask] = label
+
+        return (output_np_smoothed, 'labels-vb')
+    
+
+    def __call__(self, input_images_file):
+        time_start = time()
+        worker_name = multiprocessing.current_process().name
+        logging.debug(f"Processing `{input_images_file}` @{worker_name}")
+
+        try:
+            # load case
+            case = CaseLoader(self.root, input_images_file, self.labels_map)
+
+            # run pipeline according to method
+            output_np, output_dir = getattr(self, self.method)(case)
+
+            # skip patient if no output available from some reasons
+            if output_np is False or output_np.size == 0:
+                raise ValueError(f"no output available, skipping")
+            if not output_dir:
+                raise RuntimeError(f"directory for output was undefined")
+
+            # save output, logging
+            os.makedirs(self.root / output_dir, exist_ok=True)
+            output_path = self.root / output_dir / input_images_file
+            output_nib = nib.Nifti1Image(output_np, case.image_reoriented_affine)
+
+            # transform back to original orientation
+            affine_transformer = ornt_transform(axcodes2ornt("RAS"), # on purpose using RAS directly and not _reoriented_affine = additional check
+                                                io_orientation(case.image_original_affine))
+            output_nib = output_nib.as_reoriented(affine_transformer)
+            if not np.allclose(case.image_original_affine, output_nib.affine):
+                raise ValueError(f'Affine transformation failed: \n {case.image_original_affine} != \n {output_nib.affine}')
+
+            nib.save(output_nib, output_path)
+            logging.debug(f"  saved `{output_path}` ({time()-time_start:.2f}s)")
+            logging.info(f"{input_images_file} finished @{worker_name} ({time()-time_start:.2f}s)")
+
+        except Exception as e:
+            logging.warning(f"{input_images_file} failed:\n {e}\n {traceback.format_exc()}\n")
+
+
+class CaseLoader:
+    def __init__(self, input_root, input_images_file, input_labels_map):
+
+        # load source image, save shape and affine
+        input_path = input_root / 'images' / input_images_file
+        if not os.path.exists(input_path):
+            ValueError(f"{input_path} not available")
+        input_original_nib = nib.load(input_path)
+        input_reoriented_nib = nib.as_closest_canonical(input_original_nib)
+        input_reoriented_np = input_reoriented_nib.get_fdata().astype(np.float32)
+        logging.debug(f"  loaded input `{input_path}`")
+
+        # set shape and affine
+        self.image_reoriented_np = input_reoriented_np
+        self.image_reoriented_shape = input_reoriented_np.shape
+        self.image_reoriented_affine = input_reoriented_nib.affine
+        self.image_reoriented_zooms = input_reoriented_nib.header.get_zooms()
+        self.image_original_affine = input_original_nib.affine
+        self.input_images_file = input_images_file
+        self.root = input_root
+        self.labels_map = input_labels_map
+
+    def fetch_label(self, label):
+        # load label
+        label_path = self.root / label / self.input_images_file
+        label_original_nib = nib.load(label_path)
+        label_reoriented_nib = nib.as_closest_canonical(label_original_nib)
+        label_reoriented_np = label_reoriented_nib.get_fdata().astype(np.uint8)
+
+        # shape and affine checks
+        if not np.allclose(self.image_reoriented_affine, label_reoriented_nib.affine, rtol=1e-03, atol=1e-04):
+            raise ValueError(f"affine matrices of input and label {label} do not match:\n{self.image_reoriented_affine}\n{label_reoriented_nib.affine}\n{self.image_reoriented_affine-label_reoriented_nib.affine}")
+        if not np.array_equal(self.image_reoriented_shape, label_reoriented_np.shape):
+            raise ValueError(f"shapes of input and label {label} do not match: {self.image_reoriented_shape} vs {label_reoriented_np.shape}")
+
+        # load labels, create mapping array for relabeling, not existing labels are replaced with 0
+        labels = self.labels_map[label]
+        labels_max = max(max(labels.keys()), np.max(label_reoriented_np))
+        relabel_array = np.zeros(labels_max+1, dtype=np.uint8)
+        for key, value in labels.items():
+           relabel_array[key] = value
+            
+        # remap labels using fancy indexing
+        label_reoriented_np = relabel_array[label_reoriented_np]
+
+        logging.debug(f"  loaded label `{label_path}` using {len(labels)} labels")
+        return label_reoriented_np
+
+
+def main(input_root, input_prefix, method):
+    time_start = time()
+
+    # print
+    logging.info('PIPELINE: MASK PROCESSING')
+    logging.info(f'input root directory: `{input_root}`')
+    logging.info(f'method: `{method}`')
+    logging.info(f'input prefix: `{input_prefix}`')
+
+    # checks: input directory? method?
+    if not os.path.exists(input_root):
+        raise ValueError(f'Input root directory `{input_root}` does not exist.')
+    if method not in input_labels_map.keys():
+        raise ValueError(f"Method `{method}` not available.")
+
+    # find cases (images/*, prefix*nii.gz)
+    input_images_dir = input_root / "images"
+    input_images_files = [file.name for file in input_images_dir.glob(input_prefix + '*.nii.gz')]
+    logging.info(f"{len(input_images_files)} input images identified")
+
+    # identifying all input subdirectories
+    input_labels_required = input_labels_map[method]
+    logging.info(f"{len(input_labels_required)} labels required: {', '.join(input_labels_required.keys())}")
+
+    # check if all required label files are available
+    input_images_files = [file for file in input_images_files if all((input_root / label / file).exists() for label in input_labels_required.keys())]
+    logging.info(f"{len(input_images_files)} complete cases (all required labels available) identified")
+
+    # initialize current method as process
+    process = ProcessLoader(input_root, method)
+    n_processes = min(multiprocessing.cpu_count(), len(input_images_files))
+
+    # multiprocessing
+    logging.info(f"spawn processes at {n_processes}/{multiprocessing.cpu_count()} CPUs\n")
+    with multiprocessing.Pool(processes=n_processes, maxtasksperchild=20) as p:
+       p.map(process, input_images_files, chunksize=min(5, n_processes))
+
+    # finalized
+    logging.info(f"FINISHED PIPELINE ({len(input_images_files)} cases in {time()-time_start:.2f}s)")
+
+
+if __name__ == "__main__":
+    """
+    Toolkit for label manipulation, combination and agregation.
+    requires directories images/, and directories corresponding to the labels as specified in input_labels_map within the input root directory.
+
+    Usage:
+    suppl/4_MaskEdits.py -i /Volumes/path/to/main/ -m spine_to_vb
+    """
+
+    parser = argparse.ArgumentParser(description="toolkit to combine and manipulate masks.")
+    parser.add_argument("-i", "--input", metavar="Input root directory", dest="input_root",
+                        help="Root Directory", 
+                        type=lambda p: Path(p).absolute(), required=True)
+    parser.add_argument("-b", "--batch", metavar="Prefix of inputs", dest="input_prefix",
+                        help="Prefix of input files to be processed", 
+                        type=str, required=False, default="")   
+    parser.add_argument("-m", "--method", metavar="Method", dest="method",
+                        help="The method / pipeline used for mask processing",
+                        type=str, choices=["spine_to_vb"], required=True)
+    args = parser.parse_args()
+
+    main(input_root=args.input_root, input_prefix=args.input_prefix, method=args.method)

suppl/5_splits.md

@@ -0,0 +1,12 @@
+# Splits
+
+After labeling, the dataset was divided into six parts: one for testing and five for use as folds in a cross-validation training scenarios. The split was stratified based on
+- Primary Stratification: Data source (TotalSegmentator vs. VerSe) to ensure that each split reflects the original datasets.
+- Secondary Stratification: Vertebrae present in each scan, to maintain a balanced distribution of anatomical structures across the splits.
+
+Additionally, CT scans that were originally assigned to the test sets (73 from TotalSegmentator and 105 from VerSe) were preferentially included in the test set of this dataset. To address the rarity of certain labels, we prioritized an equal distribution of these rare labels over the more common vertebrae.
+
+For detailed reference, we provide the following files:
+- [data.csv](../data.csv): This file provides an overview of the vertebrae present in each case, the original assignment sets (`set_original`, either training or testing), the newly assigned set (`set`, either training or testing), and the specific fold (`fold`, -1 for testing and 0-4 for individual training folds) to which each case was assigned.
+- [splits_train-test.json]: This file contains the split into training and test sets.
+- [splits_folds.json]: This file details the split into training and cross-validation sets. It is equivalent to the splits-final.json file used in nnU-Net.

suppl/splits_train-test.json

@@ -0,0 +1,1466 @@
+{
+   "train": [
+      "tseg-s0003.nii.gz",
+      "tseg-s0009.nii.gz",
+      "tseg-s0010.nii.gz",
+      "tseg-s0011.nii.gz",
+      "tseg-s0012.nii.gz",
+      "tseg-s0014.nii.gz",
+      "tseg-s0015.nii.gz",
+      "tseg-s0016.nii.gz",
+      "tseg-s0019.nii.gz",
+      "tseg-s0022.nii.gz",
+      "tseg-s0024.nii.gz",
+      "tseg-s0028.nii.gz",
+      "tseg-s0030.nii.gz",
+      "tseg-s0031.nii.gz",
+      "tseg-s0032.nii.gz",
+      "tseg-s0034.nii.gz",
+      "tseg-s0035.nii.gz",
+      "tseg-s0036.nii.gz",
+      "tseg-s0037.nii.gz",
+      "tseg-s0039.nii.gz",
+      "tseg-s0042.nii.gz",
+      "tseg-s0045.nii.gz",
+      "tseg-s0046.nii.gz",
+      "tseg-s0049.nii.gz",
+      "tseg-s0050.nii.gz",
+      "tseg-s0052.nii.gz",
+      "tseg-s0053.nii.gz",
+      "tseg-s0054.nii.gz",
+      "tseg-s0056.nii.gz",
+      "tseg-s0057.nii.gz",
+      "tseg-s0058.nii.gz",
+      "tseg-s0059.nii.gz",
+      "tseg-s0063.nii.gz",
+      "tseg-s0065.nii.gz",
+      "tseg-s0068.nii.gz",
+      "tseg-s0069.nii.gz",
+      "tseg-s0070.nii.gz",
+      "tseg-s0071.nii.gz",
+      "tseg-s0072.nii.gz",
+      "tseg-s0073.nii.gz",
+      "tseg-s0074.nii.gz",
+      "tseg-s0075.nii.gz",
+      "tseg-s0076.nii.gz",
+      "tseg-s0077.nii.gz",
+      "tseg-s0078.nii.gz",
+      "tseg-s0080.nii.gz",
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