# Implicit Shape-Prior for Few-Shot Assisted 3D Segmentation

arXiv: https://arxiv.org/abs/2509.08580 · HTML: https://mlsanity.com/paper/2509.08580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 10 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023)
- [DeepSSM: A Blueprint for Image-to-Shape Deep Learning Models](https://mlsanity.com/paper/2110.07152/) (2021)
- [DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images](https://mlsanity.com/paper/2305.10655/) (2023)
- [MedUHIP: Towards Human-In-the-Loop Medical Segmentation](https://mlsanity.com/paper/2408.01620/) (2024)
- [Abdominal organ segmentation via deep diffeomorphic mesh deformations](https://mlsanity.com/paper/2306.15515/) (2023)
- [Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images](https://mlsanity.com/paper/2405.09697/) (2024)
- [Interactive Image Selection and Training for Brain Tumor Segmentation Network](https://mlsanity.com/paper/2406.03225/) (2024)
- [PE-MED: Prompt Enhancement for Interactive Medical Image Segmentation](https://mlsanity.com/paper/2308.13746/) (2023)
- [MASSM: An End-to-End Deep Learning Framework for Multi-Anatomy Statistical Shape Modeling Directly From Images](https://mlsanity.com/paper/2403.11008/) (2024)
- [Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations](https://mlsanity.com/paper/2409.07100/) (2024)
- [A dynamic interactive learning framework for automated 3D medical image segmentation](https://mlsanity.com/paper/2312.06072/) (2023)
- [Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine](https://mlsanity.com/paper/2411.19447/) (2024)
- [Unsupervised Anomaly Detection on Implicit Shape representations for Sarcopenia Detection](https://mlsanity.com/paper/2502.09088/) (2025)

## Abstract (arXiv)

The objective of this paper is to significantly reduce the manual workload required from medical professionals in complex 3D segmentation tasks that cannot be yet fully automated. For instance, in radiotherapy planning, organs at risk must be accurately identified in computed tomography (CT) or magnetic resonance imaging (MRI) scans to ensure they are spared from harmful radiation. Similarly, diagnosing age-related degenerative diseases such as sarcopenia, which involve progressive muscle volume loss and strength, is commonly based on muscular mass measurements often obtained from manual segmentation of medical volumes. To alleviate the manual-segmentation burden, this paper introduces an implicit shape prior to segment volumes from sparse slice manual annotations generalized to the multi-organ case, along with a simple framework for automatically selecting the most informative slices to guide and minimize the next interactions. The experimental validation shows the method's effectiveness on two medical use cases: assisted segmentation in the context of at risks organs for brain cancer patients, and acceleration of the creation of a new database with unseen muscle shapes for patients with sarcopenia.