TITLE: A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data 

ABSTRACT: With the advent of deep learning algorithms, fully automated radiological
image analysis is within reach. In spine imaging, several atlas- and
shape-based as well as deep learning segmentation algorithms have been
proposed, allowing for subsequent automated analysis of morphology and
pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019)
showed that these perform well on normal anatomy, but fail in variants not
frequently present in the training dataset. Building on that experience, we
report on the largely increased VerSe 2020 dataset and results from the second
iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020
comprises annotated spine computed tomography (CT) images from 300 subjects
with 4142 fully visualized and annotated vertebrae, collected across multiple
centres from four different scanner manufacturers, enriched with cases that
exhibit anatomical variants such as enumeration abnormalities (n=77) and
transitional vertebrae (n=161). Metadata includes vertebral labelling
information, voxel-level segmentation masks obtained with a human-machine
hybrid algorithm and anatomical ratings, to enable the development and
benchmarking of robust and accurate segmentation algorithms.
With the advent of deep learning algorithms, fully automated radiological
image analysis is within reach. In spine imaging, several atlas- and
shape-based as well as deep learning segmentation algorithms have been
proposed, allowing for subsequent automated analysis of morphology and
pathology. The first Large Scale Vertebrae Segmentation Challenge (VerSe 2019)
showed that these perform well on normal anatomy, but fail in variants not
frequently present in the training dataset. Building on that experience, we
report on the largely increased VerSe 2020 dataset and results from the second
iteration of the VerSe challenge (MICCAI 2020, Lima, Peru). VerSe 2020
comprises annotated spine computed tomography (CT) images from 300 subjects
with 4142 fully visualized and annotated vertebrae, collected across multiple
centres from four different scanner manufacturers, enriched with cases that
exhibit anatomical variants such as enumeration abnormalities (n=77) and
transitional vertebrae (n=161). Metadata includes vertebral labelling
information, voxel-level segmentation masks obtained with a human-machine
hybrid algorithm and anatomical ratings, to enable the development and
benchmarking of robust and accurate segmentation algorithms.
A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data
http://arxiv.org/abs/2103.06360v1