mlsanityData to 7 Oct 2026

Implicit U-Net for volumetric medical image segmentation

arXiv 2206.15217 ↗First posted 30 Jun 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combining a convolutional feature extractor with an implicit localization network, our implicit U-Net has 40% less parameters than the equivalent U-Net. Moreover, we propose training and inference procedures to capitalize sparse predictions. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time as well as training memory footprint while achieving comparable results in our experiments with two different abdominal CT scan datasets.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 30 Jun 2022.

Builds on

16 of 24 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  5. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  6. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  7. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  8. 2021The Medical Segmentation Decathloncited by 620
  9. 2014Deeply-Supervised Netscited by 364
  10. 2021CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentationcited by 227
  11. 2021IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representationcited by 42
  12. 2018RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scanscited by 41
4 more
  1. 2017Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalitiescited by 25
  2. 2017Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentationcited by 18
  3. 2021Implicit field learning for unsupervised anomaly detection in medical imagescited by 17
  4. 2021Efficient Context-Aware Network for Abdominal Multi-organ Segmentationcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstructioncited by 1
  2. 2025Template-Guided Reconstruction of Pulmonary Segments with Neural Implicit Functionscited by 0
  3. 2025End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Datacited by 0
  4. 2026Pix2Rep-v2: Data-Efficient Representation Learning for Dense Medical Imaging Applicationscited by 0