# SPINEPS -- Automatic Whole Spine Segmentation of T2-weighted MR images using a Two-Phase Approach to Multi-class Semantic and Instance Segmentation

arXiv: https://arxiv.org/abs/2402.16368 · HTML: https://mlsanity.com/paper/2402.16368/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Panoptic Segmentation](https://mlsanity.com/paper/1801.00868/) (2018)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Lumbar spine segmentation in MR images: a dataset and a public benchmark](https://mlsanity.com/paper/2306.12217/) (2023)
- [Extending nnU-Net is all you need](https://mlsanity.com/paper/2208.10791/) (2022)
- [From CNN to Transformer: A Review of Medical Image Segmentation Models](https://mlsanity.com/paper/2308.05305/) (2023)
- [Denoising diffusion-based MRI to CT image translation enables automated spinal segmentation](https://mlsanity.com/paper/2308.09345/) (2023)
- [Panoptica -- instance-wise evaluation of 3D semantic and instance segmentation maps](https://mlsanity.com/paper/2312.02608/) (2023)
- [Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior](https://mlsanity.com/paper/1804.01307/) (2018)
- [Automatic Semantic Segmentation of the Lumbar Spine: Clinical Applicability in a Multi-parametric and Multi-centre Study on Magnetic Resonance Images](https://mlsanity.com/paper/2111.08712/) (2021)

## Built on by (most cited first, 9 of 9)

- [VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank](https://mlsanity.com/paper/2406.00125/) (2024)
- [SLoRD: Structural Low-Rank Descriptors for Shape Consistency in Vertebrae Segmentation](https://mlsanity.com/paper/2407.08555/) (2024)
- [BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis](https://mlsanity.com/paper/2507.09036/) (2025)
- [VERIDAH: Solving Enumeration Anomaly Aware Vertebra Labeling across Imaging Sequences](https://mlsanity.com/paper/2601.14066/) (2026)
- [Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine](https://mlsanity.com/paper/2508.14708/) (2025)
- [EXR: An Interactive Immersive EHR Visualization in Extended Reality](https://mlsanity.com/paper/2512.05438/) (2025)
- [SpineReport: Automated 3D Quantification and Reporting of Lumbar Spine Degeneration on MRI](https://mlsanity.com/paper/2606.10021/) (2026)
- [Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading](https://mlsanity.com/paper/2608.04810/) (2026)
- [CTSpinoPelvic1K: spine, pelvis, ribs and femora in one coordinate frame, annotated for lumbosacral transitional anatomy](https://mlsanity.com/paper/2609.22760/) (2026)

## Abstract (arXiv)

Purpose. To present SPINEPS, an open-source deep learning approach for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole body T2w MRI. Methods. During this HIPPA-compliant, retrospective study, we utilized the public SPIDER dataset (218 subjects, 63% female) and a subset of the German National Cohort (1423 subjects, mean age 53, 49% female) for training and evaluation. We combined CT and T2w segmentations to train models that segment 14 spinal structures in T2w sagittal scans both semantically and instance-wise. Performance evaluation metrics included Dice similarity coefficient, average symmetrical surface distance, panoptic quality, segmentation quality, and recognition quality. Statistical significance was assessed using the Wilcoxon signed-rank test. An in-house dataset was used to qualitatively evaluate out-of-distribution samples. Results. On the public dataset, our approach outperformed the baseline (instance-wise vertebra dice score 0.929 vs. 0.907, p-value<0.001). Training on auto-generated annotations and evaluating on manually corrected test data from the GNC yielded global dice scores of 0.900 for vertebrae, 0.960 for intervertebral discs, and 0.947 for the spinal canal. Incorporating the SPIDER dataset during training increased these scores to 0.920, 0.967, 0.958, respectively. Conclusions. The proposed segmentation approach offers robust segmentation of 14 spinal structures in T2w sagittal images, including the spinal cord, spinal canal, intervertebral discs, endplate, sacrum, and vertebrae. The approach yields both a semantic and instance mask as output, thus being easy to utilize. This marks the first publicly available algorithm for whole spine segmentation in sagittal T2w MR imaging.