SomaNet: Weakly Supervised Learning for Instance Soma Segmentation in 3D Electron Microscopy with Partial Annotations

arXiv:2609.23019 · eess.IV, cs.CV · Submitted 2026-09-19 · Read on arXiv

eess.IV, cs.CV

Submitted: 2026-09-19

Updated: 2026-09-19

Code: https://github.com/mkhateri/SomaNet

Terminology

• Instance Segmentation — 60× in this paper · explained in 3 episodes
• Consistency — 15× in this paper · explained in 3 episodes
• Embeddings — 10× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Pseudo-labels — 9× in this paper · explained in 2 episodes
• Foundation Model — 7× in this paper · explained in 3 episodes
• Semi-supervised Learning — 7× in this paper · explained in 4 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Distillation — 4× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Panoptic Quality — 1× in this paper
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Semi-Supervised Learning (SSL) — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Variation of Information — 1× in this paper

Sources

• DINOv2: Learning Robust Visual Features without Supervision
• Semantic Instance Segmentation with a Discriminative Loss Function
• Superhuman Accuracy on the SNEMI3D Connectomics Challenge
• ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
• Vision Transformer Adapter for Dense Predictions

Related papers

• Revisiting Integration of Image and Metadata for DICOM Series Classification: Cross-Attention and Dictionary Learning
• VesselSDF: Distance Field Priors for Vascular Network Reconstruction
• cSVR: Convolutional Slice-to-Volume Reconstruction
• NAIMA: Semantics Aware RGB Guided Depth Super-Resolution
• AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFD
• RETO: A Rotary-Enhanced Transformer Operator for High-Fidelity Prediction of Automotive Aerodynamics