mlsanityData to 14 Oct 2026

ShapeKit

arXiv 2506.24003 ↗First posted 30 Jun 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

In this paper, we present a practical approach to improve anatomical shape accuracy in whole-body medical segmentation. Our analysis shows that a shape-focused toolkit can enhance segmentation performance by over 8%, without the need for model re-training or fine-tuning. In comparison, modifications to model architecture typically lead to marginal gains of less than 3%. Motivated by this observation, we introduce ShapeKit, a flexible and easy-to-integrate toolkit designed to refine anatomical shapes. This work highlights the underappreciated value of shape-based tools and calls attention to their potential impact within the medical segmentation community.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 38 references that are in the map. First posted on arXiv 30 Jun 2025.

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26 of 38 references.

References · 26

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
  3. 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 586
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 448
  5. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260
  6. 2024nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentationcited by 240
  7. 2020AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?cited by 224
  8. 2023CLIP-Driven Universal Model for Organ Segmentation and Tumor Detectioncited by 224
  9. 2024MAISI: Medical AI for Synthetic Imagingcited by 87
  10. 2024Towards Generalizable Tumor Synthesiscited by 65
  11. 2024AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarkingcited by 65
  12. 2023Label-Free Liver Tumor Segmentationcited by 56
14 more
  1. 2023Continual Learning for Abdominal Multi-Organ and Tumor Segmentationcited by 43
  2. 2025RadGPT: Constructing 3D Image-Text Tumor Datasetscited by 36
  3. 2025Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planningcited by 34
  4. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 32
  5. 2024Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomographycited by 31
  6. 2021Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Drivingcited by 23
  7. 2024From Pixel to Cancer: Cellular Automata in Computed Tomographycited by 20
  8. 2023Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Promptscited by 17
  9. 2024Text-Driven Tumor Synthesiscited by 13
  10. 2025MedSegFactory: Text-Guided Generation of Medical Image-Mask Pairscited by 13
  11. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  12. 2024Leveraging AI Predicted and Expert Revised Annotations in Interactive Segmentation: Continual Tuning or Full Training?cited by 9
  13. 2025Expectation-Maximization as the Engine of Scalable Medical Intelligencecited by 5
  14. 2024Label Critic: Design Data Before Modelscited by 4

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