tutorials

Tutorial Configs

Top-level tutorial configs are in this folder and are intended to be runnable with:

python scripts/main.py --config tutorials/<config>.yaml

Active top-level configs

• tutorials/mito_lucchi++.yaml: Lucchi++ mitochondria segmentation (MONAI UNet).
• tutorials/mito_mitoEM.yaml: Backward-compatible alias to mito_mitoEM_30h.yaml.
• tutorials/mito_mitoEM_30h.yaml: MitoEM-Human (EM30-H) instance segmentation (MedNeXt, SDT).
• tutorials/mito_mitoEM_30r.yaml: MitoEM-Rat (EM30-R) instance segmentation (MedNeXt, SDT).
• tutorials/mito_mitoEM_30hr.yaml: Joint EM30-H + EM30-R training (MedNeXt, SDT).
• tutorials/mito_mitolab.yaml: CEM-MitoLab 2D mitochondria segmentation (MedNeXt).
• tutorials/mito_betaseg.yaml: BetaSeg mitochondria instance segmentation (MedNeXt, affinity+SDT).
• tutorials/neuron_snemi.yaml: SNEMI3D neuron segmentation (RSUNet, affinities).
• tutorials/nuc_nucmm-z.yaml: NucMM zebrafish nuclei segmentation (MONAI UNet, multi-task).
• tutorials/fiber_linghu26.yaml: Fiber segmentation (MedNeXt, binary+boundary+distance).

Config composition (_base_)

Top-level configs now use inheritance via _base_:

• tutorials/bases/common.yaml: Shared defaults across top-level tutorials.
• tutorials/bases/arch_profiles.yaml: Architecture profile presets (mednext_s, mednext_b, mednext_m, mednext_l, monai_unet, rsunet).
• tutorials/bases/loss_profiles.yaml: Reusable loss presets (for example loss_bcd).
• Top-level tutorials should keep selector-only shared keys (for example shared.arch_profile, shared.loss_profile).

_base_ supports:

• A single file path (_base_: bases/common.yaml)
• A list of files (_base_: [a.yaml, b.yaml]) with left-to-right merge order
• Relative paths resolved from the current config file

Validation

Validate top-level tutorial configs:

python scripts/validate_tutorial_configs.py

This check fails if a config cannot load or if legacy keys reappear (inference.data, data.augmentation.enabled, or inference.test_time_augmentation.act).