# BOSS: Bones, Organs and Skin Shape Model

arXiv: https://arxiv.org/abs/2303.04923 · HTML: https://mlsanity.com/paper/2303.04923/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 52 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images](https://mlsanity.com/paper/2001.09193/) (2020)
- [Building Statistical Shape Spaces for 3D Human Modeling](https://mlsanity.com/paper/1503.05860/) (2015)
- [OSSO: Obtaining Skeletal Shape from Outside](https://mlsanity.com/paper/2204.10129/) (2022)
- [XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms](https://mlsanity.com/paper/2007.13408/) (2020)

## Built on by (most cited first, 12 of 12)

- [From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans](https://mlsanity.com/paper/2509.06607/) (2025)
- [ATLAS: Decoupling Skeletal and Shape Parameters for Expressive Parametric Human Modeling](https://mlsanity.com/paper/2508.15767/) (2025)
- [MHR: Momentum Human Rig](https://mlsanity.com/paper/2511.15586/) (2025)
- [LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images](https://mlsanity.com/paper/2406.12407/) (2024)
- [UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation](https://mlsanity.com/paper/2504.06908/) (2025)
- [Internal Organ Localization Using Depth Images](https://mlsanity.com/paper/2503.23468/) (2025)
- [Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface](https://mlsanity.com/paper/2505.22511/) (2025)
- [Depth to Anatomy: Organ Localization from Depth Images for Automated Patient Table Positioning in Radiology Workflow](https://mlsanity.com/paper/2601.18260/) (2026)
- [SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound](https://mlsanity.com/paper/2604.25646/) (2026)
- [BioHuman: Learning Biomechanical Human Representations from Video](https://mlsanity.com/paper/2605.14772/) (2026)
- [Statistical Hand Shape Modeling from Clinical CT Scans Using Deep Learning and Implicit Skinning](https://mlsanity.com/paper/2605.16980/) (2026)
- [Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan](https://mlsanity.com/paper/2607.02156/) (2026)

## Abstract (arXiv)

Objective: A digital twin of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. However, it is crucial that the patient's surface and internal organs are of high quality for any pose and shape estimates. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. Method: To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can benefit various medical applications. Results: We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. Conclusion: This works shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. Significance: The proposed approach enables the construction of shape models that can be directly applied to various medical applications, including biomechanics and reconstruction.