README.md

SegNeuron

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

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.