Name: RSNA 2022 Spine Fracture Detection - Metadata
Creator: Kaggle
License: https://creativecommons.org/publicdomain/zero/1.0/

About this Dataset

RSNA 2022 Spine Fracture Detection - Metadata

This dataset contains metadata extracted from train image dicom files relevant to the RSNA 2022 Cervical Spine Fracture Detection competition.

meta-train - original metadata extracted (ignore this file)
meta-train-clean - cleaned version of meta-train (easier to use)
meta-segmentations - meta-data for images with segmentations (including correct labels C1-C7 extracted from unique values in segmentations)
meta-segmentation-clean - cleaned version of meta-segmentations.
meta-train-with-vertebrae - meta-data for all train images (with 88% accurate RF predictions of which vertebrae is in each image)
train-segmented - meta-data for all train images (with 95% accurate EffNetV2 predictions of which vertebrae is in each image from this notebook)
train-vert-fold4 - this is similar to 6. train-segmented, made by cleaning the segmentations and training an image+tabular model. Also includes additional columns created from feature engineering.
train-vert - ensembled predictions of train-segmented and train-vert-fold4.

The notebooks used to create these files are below:

Tables

Meta Segmentation

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.meta_segmentation

161.5 KB
29832 rows
15 columns


CREATE TABLE meta_segmentation (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "c1" BIGINT,
  "c2" BIGINT,
  "c3" BIGINT,
  "c4" BIGINT,
  "c5" BIGINT,
  "c6" BIGINT,
  "c7" BIGINT
);

Meta Segmentation Clean

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.meta_segmentation_clean

161.18 KB
29832 rows
15 columns


CREATE TABLE meta_segmentation_clean (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "c1" BIGINT,
  "c2" BIGINT,
  "c3" BIGINT,
  "c4" BIGINT,
  "c5" BIGINT,
  "c6" BIGINT,
  "c7" BIGINT
);

Meta Train

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.meta_train

7.87 MB
711601 rows
8 columns


CREATE TABLE meta_train (
  "rows" BIGINT,
  "columns" BIGINT,
  "sopinstanceuid" VARCHAR,
  "contentdate" BIGINT,
  "slicethickness" DOUBLE,
  "instancenumber" BIGINT,
  "imagepositionpatient" VARCHAR,
  "imageorientationpatient" VARCHAR
);

Meta Train Clean

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.meta_train_clean

2.85 MB
711601 rows
8 columns


CREATE TABLE meta_train_clean (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE
);

Meta Train With Vertebrae

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.meta_train_with_vertebrae

5.3 MB
711601 rows
16 columns


CREATE TABLE meta_train_with_vertebrae (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "sliceratio" DOUBLE,
  "c1" BIGINT,
  "c2" BIGINT,
  "c3" BIGINT,
  "c4" BIGINT,
  "c5" BIGINT,
  "c6" BIGINT,
  "c7" BIGINT
);

Train Segmented

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.train_segmented

17.68 MB
711601 rows
15 columns


CREATE TABLE train_segmented (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "c1" DOUBLE,
  "c2" DOUBLE,
  "c3" DOUBLE,
  "c4" DOUBLE,
  "c5" DOUBLE,
  "c6" DOUBLE,
  "c7" DOUBLE
);

Train Vert

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.train_vert

45.44 MB
711601 rows
18 columns


CREATE TABLE train_vert (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "sliceratio" DOUBLE,
  "slicetotal" BIGINT,
  "reversed" BIGINT,
  "c1" DOUBLE,
  "c2" DOUBLE,
  "c3" DOUBLE,
  "c4" DOUBLE,
  "c5" DOUBLE,
  "c6" DOUBLE,
  "c7" DOUBLE
);

Train Vert Fold4

@kaggle.samuelcortinhas_rsna_2022_spine_fracture_detection_metadata.train_vert_fold4

46.56 MB
711601 rows
18 columns


CREATE TABLE train_vert_fold4 (
  "studyinstanceuid" VARCHAR,
  "slice" BIGINT,
  "imageheight" BIGINT,
  "imagewidth" BIGINT,
  "slicethickness" DOUBLE,
  "imagepositionpatient_x" DOUBLE,
  "imagepositionpatient_y" DOUBLE,
  "imagepositionpatient_z" DOUBLE,
  "sliceratio" DOUBLE,
  "slicetotal" BIGINT,
  "reversed" BIGINT,
  "c1" DOUBLE,
  "c2" DOUBLE,
  "c3" DOUBLE,
  "c4" DOUBLE,
  "c5" DOUBLE,
  "c6" DOUBLE,
  "c7" DOUBLE
);