mlsanityData to 7 Oct 2026

YOLO2U-Net: Detection-Guided 3D Instance Segmentation for Microscopy

arXiv 2207.06215 ↗First posted 13 Jul 2022eess.IV cs.CV q-bio.CBcited by 1

Not on the map. Why?

Abstract (arXiv)

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. In this work, we introduce a comprehensive method for accurate 3D instance segmentation of cells in the brain tissue. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with some current deep learning-based 3D instance segmentation methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 13 Jul 2022.

Builds on

15 of 28 references.

References · 15

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2017Mask R-CNNcited by 7,259
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2016Pyramid Scene Parsing Networkcited by 2,472
  6. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  7. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  8. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  9. 2021Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentationcited by 1,218
  10. 2016Deep Watershed Transform for Instance Segmentationcited by 77
  11. 2019Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopycited by 45
  12. 20163D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumescited by 43
3 more
  1. 2016Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentationcited by 25
  2. 2019Mask-RCNN and U-net Ensembled for Nuclei Segmentationcited by 23
  3. 2018BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Maskscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Toward a Foundation Plug-and-Play Prior for Computed Tomography Reconstruction via a Multimodal Diffusion Modelcited by 0