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AnikS22/
MidasMap

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MidasMap: Immunogold Particle Detection for TEM Synapse Images

MidasMap automatically detects 6nm (AMPA receptor) and 12nm (NR1/NMDA receptor) immunogold particles in freeze-fracture replica immunolabeling (FFRIL) transmission electron microscopy images.

Performance

Metric	Value
LOOCV Mean F1	0.943 (8 folds with sufficient annotations)
6nm (AMPA) F1	0.944 (100% recall)
12nm (NR1) F1	0.909 (100% recall)
Parameters	24.4M
Inference	~10s per image (GPU)

Validated on 453 labeled particles across 10 synapse images via leave-one-image-out cross-validation with 5 random seeds per fold.

Quick Start

python

import torch
from src.model import ImmunogoldCenterNet
from src.ensemble import sliding_window_inference
from src.heatmap import extract_peaks
from src.postprocess import cross_class_nms
import tifffile

# Load model
model = ImmunogoldCenterNet(bifpn_channels=128, bifpn_rounds=2)
ckpt = torch.load("checkpoints/final/final_model.pth", map_location="cpu")
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

# Run on any TEM image
img = tifffile.imread("your_image.tif")
if img.ndim == 3:
    img = img[:, :, 0]

with torch.no_grad():
    hm, off = sliding_window_inference(model, img, patch_size=512, overlap=128)

dets = extract_peaks(torch.from_numpy(hm), torch.from_numpy(off),
                     stride=2, conf_threshold=0.25)
dets = cross_class_nms(dets, 8)

for d in dets:
    print(f"{d['class']} at ({d['x']:.1f}, {d['y']:.1f}) conf={d['conf']:.3f}")

Web Dashboard

bash

pip install gradio
python app.py --checkpoint checkpoints/final/final_model.pth
# Opens at http://localhost:7860

Upload TIF images, adjust confidence threshold, view heatmaps, and export CSV results.

Architecture

Raw TEM Image (any size)
    |
[Sliding window: 512x512, 128px overlap]
    |
ResNet-50 (CEM500K pretrained on 500K EM images)
    |
BiFPN (bidirectional feature pyramid, 2 rounds, 128ch)
    |
Transposed Conv → stride-2 output (H/2 x W/2)
    |
+--Heatmap Head (2ch sigmoid: 6nm + 12nm)
+--Offset Head (2ch: sub-pixel x,y correction)
    |
Peak extraction (max-pool NMS) → detections

Key Design Choices

CEM500K backbone: Pretrained on 500,000 electron microscopy images. Reaches F1=0.93 in just 5 training epochs because it already understands EM structures.
Stride-2 output: Standard CenterNet uses stride 4, but 6nm beads (4-6px radius) collapse to 1px at that resolution. Stride 2 preserves 2-3px per bead.
CornerNet focal loss: Handles the extreme class imbalance (positive:negative pixel ratio ~1:23,000).
Raw image input: No preprocessing — CEM500K was trained on raw EM, so any heavy filtering creates a domain gap.

Training

3-Phase Strategy

Phase 1 (40 epochs): Freeze encoder, train BiFPN + heads at lr=1e-3
Phase 2 (40 epochs): Unfreeze layer3+4 at lr=1e-5 to 5e-4
Phase 3 (60 epochs): Full fine-tune with discriminative LRs (1e-6 to 2e-4)

Data Augmentation

Random 90-degree rotations, flips
Conservative brightness/contrast (+-8%)
Gaussian noise, mild blur
Copy-paste: real bead crops blended onto training patches
70% hard mining (patches centered on particles)

Overfitting Prevention

RNG reseeded per sample (unique patches every epoch)
Early stopping (patience=20, monitoring val F1)
Weight decay 1e-4

Train Final Model

python train_final.py --config config/config.yaml --device cuda:0

HPC (SLURM)

sbatch slurm/05_train_final.sh

LOOCV Results (per fold)

Fold	Avg F1	Best F1	# Particles
S27	0.990	0.994	45
S8	0.981	0.988	70
S25	0.972	0.977	41
S29	0.956	0.966	36
S1	0.930	0.940	22
S4	0.919	0.972	113
S22	0.907	0.938	102
S13	0.890	0.912	20
S7*	0.799	1.000	3
S15*	0.633	0.667	1

*S7 and S15 have insufficient annotations for reliable evaluation (3 and 1 particles respectively).

Dataset

10 FFRIL synapse images (2048x2115 pixels)
403 labeled 6nm particles (AMPA receptors)
50 labeled 12nm particles (NR1 receptors)
Annotations in microns, converted at 1790 px/micron

Critical Implementation Notes

Coordinate conversion: CSV "XY in microns" values are actual microns, not normalized coordinates. Multiply by 1790 to get pixels.
Heatmap peaks: Must be exactly 1.0 at integer grid centers. The CornerNet focal loss uses pos_mask = (gt == 1.0).
Patch diversity: RNG must be reseeded per __getitem__ call to prevent memorizing fixed patches.

Citation

If you use MidasMap in your research, please cite:

bibtex

@software{midasmap2026,
  title={MidasMap: Automated Immunogold Particle Detection for TEM Synapse Images},
  author={Sahai, Anik},
  year={2026},
  url={https://github.com/AnikS22/MidasMap}
}

Dependencies

PyTorch >= 2.0
torchvision
albumentations
scikit-image
tifffile
CEM500K weights (download: python scripts/download_cem500k.py)