This capsule generates synthetic dendrites simulating neural activity with realistic noise and motion artifacts closely representing 2-Photon imaging data collected from mice with fluorescent glutamate indicator iGluSnFR.
Key features include:
- Neural activity modeling with configurable spike patterns and synaptic dynamics
- 3D motion simulation incorporating translational and rotational drift in XYZ planes
- Noise injection with Poisson-distributed photon noise and detector dark current
- Multi-format output supporting both TIFF stacks and HDF5 files for flexibility
This capsule requires Z-stacks to infer and apply z motion to the simulations.
CO Data Asset: b5118287-af2c-433e-bc57-d4156844cd5f
root
└── data
└── Bergamo-zStacks
├── scan_00001-REF_Ch2.ome
│ └── scan_00001-REF_Ch2.ome.tif
└── scan_00002-REF_Ch2.ome
└── scan_00002-REF_Ch2.ome.tif
.
.
.
└── scan_*-REF_Ch2.ome
└── scan_*-REF_Ch2.ome.tif
results/
└── parameters.csv
└── SimulationDescription/
├── SIMULATION_*.tif/.h5 # Raw time-lapse data
├── *_groundtruth.h5 # Ground truth coordinates/activity
└── simulation_parameters.json # Configuration metadata
- parameters.csv contains SimDescription,motionAmp,brightness,nsites,scan which are the parameters used to generate simulation data.
- *_groundtruth.h5 Key HDF5 datasets include
/GT/R,/GT/C,/GT/Zfor 3D coordinates,/GT/activityfor neural spike trains, and/GT/motionC,/GT/motionR,/GT/motionZfor motion across XYZ.
- Go to run and adjust
motionAmp_values,brightness_values,nsites_valuesto your liking. - From run change path under
subdirsif different dataset is used, if you use a different dataset you may need to changefn.endswith("_Ch2.ome.tif")from run_capsule.py
⚠️ WARNING: You may want to adjustmax_parallelbased on your system requirements.
 |
 |
|---|---|
| Before adding motion correction and noise | After adding motion correction and noise |
START
│
├─▶ [Initialization]
│ ├─▶ Parse Command Line Arguments:
│ │ - Input/output paths, simulation parameters
│ │
│ └─▶ Initialize Parameters (params):
│ - Set defaults: darkrate=0.02, nsites=30, T=10000, etc.
│
├─▶ Create Output Directory
│ └─▶ IF path doesn't exist → os.makedirs()
│
├─▶ File Handling
│ ├─▶ Search for TIFF Files:
│ │ - Check 1x/2x/8x downsampling versions
│ │ - Filter: Ends with "_Ch2.ome.tif", excludes "DENOISED"
│ │
│ └─▶ IF no files found → ERROR EXIT
│
└─▶ For Each TIFF File:
│
├─▶ Metadata Processing
│ ├─▶ Load Metadata:
│ │ - ScanImageTiffReader().metadata()
│ │
│ └─▶ Extract Pixel Resolution:
│ ├─▶ Regex search: r'pixelResolutionXY": \[(.*?)\]'
│ ├─▶ IF found → Convert to [int,int] list
│ └─▶ ELSE → Use default [125,45] → params["IMsz"] = [45,125]
│
├─▶ Load Image Volume
│ ├─▶ Read TIFF: tifffile.imread() → mov (ZXYT)
│ │
│ └─▶ Preprocessing:
│ ├─▶ Temporal Crop: mov[5:-5] (remove first/last 5 frames)
│ ├─▶ BG Subtraction: mov - 30th percentile
│ ├─▶ Normalization: ÷ 99th percentile
│ └─▶ Median Filter: 3×3×2 kernel
│
├─▶ Create Valid Region Mask
│ ├─▶ Thresholding:
│ │ - Binary mask = (filtered_data > min(97th %ile, 4×mean))
│ │
│ └─▶ Edge Exclusion:
│ - Clear borders using params["minDistance"] (mD)
│ - tmp = np.where(mask) → [Z,Y,X] candidate positions
│
├─▶ Synapse Placement (GT Generation)
│ ├─▶ IF params["nsites"] > 0:
│ │ │
│ │ ├─▶ Random Placement Mode (minDistance ≤ 0):
│ │ │ - np.random.choice() without spatial checks
│ │ │ - Add ±0.5px jitter
│ │ │
│ │ └─▶ Distance-Constrained Mode:
│ │ │
│ │ ├─▶ FOR each synapse:
│ │ │ │
│ │ │ ├─▶ WHILE trials < 10,000:
│ │ │ │ - Pick random candidate
│ │ │ │ - Check Euclidean distance ≥ minDistance
│ │ │ │ - IF valid → Place and remove from candidates
│ │ │ │
│ │ │ └─▶ ELSE → Raise ValueError
│ │ │
│ │ └─▶ Store with sub-pixel offsets (dz,dr,dc)
│ │
│ └─▶ ELSE → GT["R/C/Z"] = empty lists
│
└─▶ For Each Trial (1 to numTrials):
│
├─▶ Activity Generation
│ ├─▶ 1. Random Thresholding:
│ │ - spikes_base = (rand(nsites,T) < activityThresh)
│ │
│ ├─▶ 2. Smoothing:
│ │ - smoothed = uniform_filter1d(spikes_base, size=40) #Moving average across a window
│ │
│ ├─▶ 3. Spike Amplitude:
│ │ - spikes = (rand() < smoothed²) #Biased towards sustained activity
│ │ - amp = clip(spikeAmp×N(0,1), minspike, maxspike)
│ │
│ └─▶ 4. Calcium Convolution:
│ - kernel = exp(-t/τ)
│ - activity = convolve(spikes, kernel)
│
├─▶ Create Base Movie
│ ├─▶ 1. Initialize:
│ │ - movie = np.tile(IMVol_Avg, (1,1,1,T))
│ │
│ ├─▶ 2. Per-Synapse Processing:
│ │ │
│ │ ├─▶ a. Extract Subvolume:
│ │ │ - S = mov[zz±sw/2, rr±sw, cc±sw] #3D of each synpase
│ │ │
│ │ ├─▶ b. Shift Kernel:
│ │ │ - skernel shifted by (dz,dr,dc)
│ │ │
│ │ ├─▶ c. Filter Application:
│ │ │ - sFilt = S * shifted_kernel #Elementwise shift
│ │ │
│ │ └─▶ d. Accumulate Signals:
│ │ - movie[region] += sFilt × activity[siteN,:] #Add activity signals to movie
│ │
│ └─▶ 3. Store ROIs:
│ - idealFilts[region,siteN] = sFilt
│
├─▶ Motion Simulation
│ ├─▶ 1. Generate Motion Components:
│ │ - PC1-3 = conv(N(0,1)^3, boxcar(40)) × envelope × motionAmp #Generate per axis signals
│ │ - envelope = sin²(cumsum(N(0,1)/20)) #Slow modulation
│ │
│ ├─▶ 2. 3D Rotation:
│ │ - Build Euler matrix R(ψ,θ,φ) #Random ψ, θ, φ angles
│ │ - motion = R @ [PC1; PC2; PC3] #Apply 3D rotation
│ │
│ └─▶ 3. Scaled Motion:
│ - GT["motionR"] = 1×motion[0] (X-axis)
│ - GT["motionC"] = 0.25×motion[1] (Y-axis)
│ - GT["motionZ"] = 0.15×motion[2] (Z-axis)
│
├─▶ Apply Motion & Noise
│ ├─▶ 1. Per-Frame Processing:
│ │ │
│ │ ├─▶ a. XY Warping:
│ │ │ - cv2.warpAffine() with M = [[1,0,ΔC],[0,1,ΔR]] #ΔC and ΔR are motionC and motionR in time
│ │ │
│ │ ├─▶ b. Z Interpolation:
│ │ │ - α = fractional part of z-shift
│ │ │ - blend = (1-α)×frame_below + α×frame_above
│ │ │
│ │ └─▶ c. Photon Conversion:
│ │ - λ = blend × brightness×exp(-t/bleachTau) + darkrate
│ │
│ └─▶ 2. Noise Injection:
│ ├─▶ Poisson: Ad = poisson(λ) × photonScale
│ └─▶ Excess Noise: Ad *= clip(excessNoise, 0.5, 2)
│
└─▶ Save Outputs
├─▶ 1. Reshape Data:
│ - Ad_reshaped = Ad.transpose(3,0,1,2)
│
├─▶ 2. Write Files:
│ │
│ ├─▶ IF writetiff → tifffile.imwrite()
│ │
│ └─▶ ELSE → h5py.create_dataset(compression="gzip")
│
└─▶ 3. Save Ground Truth:
- HDF5: R/C/Z coordinates, motion vectors, activity, ROIs
- JSON: params
END
%%{init: {'themeVariables': { 'primaryColor': '#E3F2FD','edgeLabelBackground':'#FFF'}}}%%
flowchart TD;
A([Start]) --> B[Parse CLI Arguments]
B --> C[Initialize Parameters]
C --> D[Create Output Directory]
D --> E{Find TIFF Files?}
E -->|Yes| F[Process Each TIFF File]
E -->|No| G([Error Exit])
subgraph FileProcessing [TIFF Processing]
F --> H[Extract Metadata]
H --> I[Load Image Volume]
I --> J[Preprocess Data]
J --> K[Create Valid Mask]
K --> L{Synapses Needed?}
L -->|Yes| M[Place Synapses]
L -->|No| N[Skip Placement]
M --> O[Random Mode?]
O -->|Yes| P[Random Placement]
O -->|No| Q[Distance-Constrained Placement]
end
F --> R[For Each Trial]
subgraph TrialSimulation [Trial Processing]
R --> S[Generate Activity]
S --> T[Create Base Movie]
T --> U[Simulate 3D Motion]
U --> V[Apply Motion Transform]
V --> W[Add Photon Noise]
W --> X[Inject Excess Noise]
end
R --> Y[Save Outputs]
subgraph DataOutput [Output Handling]
Y --> Z[Reshape Data]
Z --> AA{TIFF Format?}
AA -->|Yes| AB[Write TIFF]
AA -->|No| AC[Write HDF5]
Y --> AD[Save Ground Truth]
AD --> AE[Store Coordinates]
AD --> AF[Save Motion Vectors]
AD --> AG[Save Activity Data]
end
Y --> A2([End])
style A fill:#4CAF50,stroke:#2E7D32
style G fill:#B71C1C,stroke:#D32F2F
style E fill:#FFC107,stroke:#FFA000
style FileProcessing fill:#E1F5FE,stroke:#039BE5
style TrialSimulation fill:#F3E5F5,stroke:#9C27B0
style DataOutput fill:#E8F5E9,stroke:#43A047
style A2 fill:#4CAF50,stroke:#2E7D32
classDef decision fill:#FFECB3,stroke:#FFA000;
classDef process fill:#B3E5FC,stroke:#039BE5;
classDef io fill:#C8E6C9,stroke:#43A047;
class E,L,O,AA decision
class B,C,D,H,I,J,K,M,N,P,Q,S,T,U,V,W,X,Y,Z,AD,AE,AF,AG process
class F,R,AB,AC,AE,AF,AG io