SegNeuron

Official implementation, datasets and trained models of "SegNeuron: 3D Neuron Instance Segmentation in Any EM Volume with a Generalist Model" （MICCAI 2024)

Environments

We have packaged all the dependencies into Connect.tar.gz, which can be directly downloaded for easy access here.

Datasets and Models

The datasets required for model development and validation are available here. The trained models can be download here.

Table: Details of EMNeuron

Dataset	Modality	Res.($nm$) ($x,y,z$)	Total voxels (M)	Labeled voxels (M)	Dataset	Modality	Res.($nm$) ($x,y,z$)	Total voxels (M)	Labeled voxels (M)
1. ZFinch	SBF-SEM	9, 9, 20	3635	131	9. HBrain	FIB-SEM	8, 8, 8	3072	844
2. ZFish	SBF-SEM	9, 9, 20	1674	-	10. FIB25	FIB-SEM	8, 8, 8	312	312
3. vEM1	ATUM-SEM	8, 8, 50	1205	157	11. Minnie	ssTEM	8, 8, 40	2096	-
4. vEM2	ATUM-SEM	8, 8, 30	1329	281	12. Pinky	ssTEM	8, 8, 40	1165	117
5. vEM3	ATUM-SEM	8, 8, 40	1301	253	13. FAFB	ssTEM	8, 8, 40	2625	577
6. MitoEM	ATUM-SEM	8, 8, 30	1048	-	14. Basil	ssTEM	8, 8, 40	23	23
7. H01	ATUM-SEM	8, 8, 30	1166	118	15. Harris	others	6, 6, 50	30	30
8. Kasthuri	ATUM-SEM	6, 6, 30	1526	478	16. vEM4	others	8, 8, 20	45	45

Training

1. Pretraining

cd Pretrain

python pretrain.py

2. Supervised Training

cd Train_and_Inference

python supervised_train.py

Inference

1. Affinity Inference

cd Train_and_Inference

python inference.py

2. Instance Segmentation

cd Postprocess

python FRMC_post.py

Acknowledgement

This code is based on SSNS-Net (IEEE TMI'22) by Huang Wei et al. The postprocessing tools are based on constantinpape/elf. Should you have any further questions, please let us know. Thanks again for your interest.