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README.md

@@ -4,8 +4,155 @@ tags:
 - lighter
 - model_hub_mixin
 - pytorch_model_hub_mixin
+language: en
+license: apache-2.0
+arxiv: 2501.09001
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: https://github.com/project-lighter/lighter-zoo
-- Docs: https://project-lighter.github.io/lighter/
+# CT-FM Feature Extractor
+
+This model is a feature extractor for CT-FM, a model for whole-body segmentation. The feature extractor is based on SegResNet, a 3D U-Net variant.
+
+If you want to just load the model and fine-tune, ignore the feature extraction workflow. 
+
+## Running instructions
+
+
+# Whole Body Segmentation Inference
+
+This notebook demonstrates how to:
+1. Load a pre-trained whole body segmentation model from HuggingFace Hub
+2. Set up preprocessing and postprocessing pipelines
+3. Perform sliding window inference on CT volumes
+4. Save the segmentation results
+
+The model segments 118 different anatomical structures from CT scans.
+
+## Setup
+Install requirements and import necessary packages
+
+
+```python
+# Install lighter_zoo package
+%pip install lighter_zoo -U -qq
+```
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+
+```python
+# Imports
+import torch
+from lighter_zoo import SegResNet
+from monai.transforms import (
+    Compose, LoadImage, EnsureType, Orientation,
+    ScaleIntensityRange, CropForeground, Invert,
+    Activations, AsDiscrete, KeepLargestConnectedComponent,
+    SaveImage
+)
+from monai.inferers import SlidingWindowInferer
+```
+
+    Note: you may need to restart the kernel to use updated packages.
+
+
+## Load Model
+Download and initialize the pre-trained model from HuggingFace Hub
+
+
+```python
+# Load pre-trained model
+model = SegResNet.from_pretrained(
+    "project-lighter/whole_body_segmentation",
+    force_download=True
+)
+```
+
+
+    config.json:   0%|          | 0.00/162 [00:00<?, ?B/s]
+
+
+
+    model.safetensors:   0%|          | 0.00/349M [00:00<?, ?B/s]
+
+
+## Configure Inference
+Set up sliding window inference for processing large volumes
+
+
+```python
+# Configure sliding window inference
+inferer = SlidingWindowInferer(
+    roi_size=[96, 160, 160],  # Size of patches to process
+    sw_batch_size=2,          # Number of windows to process in parallel
+    overlap=0.625,            # Overlap between windows (reduces boundary artifacts)
+    mode="gaussian"           # Gaussian weighting for overlap regions
+)
+```
+
+## Setup Processing Pipelines
+Define preprocessing and postprocessing transforms
+
+
+```python
+# Preprocessing pipeline
+preprocess = Compose([
+    LoadImage(ensure_channel_first=True),  # Load image and ensure channel dimension
+    EnsureType(),                         # Ensure correct data type
+    Orientation(axcodes="SPL"),           # Standardize orientation
+    # Scale intensity to [0,1] range, clipping outliers
+    ScaleIntensityRange(
+        a_min=-1024,    # Min HU value
+        a_max=2048,     # Max HU value
+        b_min=0,        # Target min
+        b_max=1,        # Target max
+        clip=True       # Clip values outside range
+    ),
+    CropForeground()    # Remove background to reduce computation
+])
+
+# Postprocessing pipeline
+postprocess = Compose([
+    Activations(softmax=True),              # Apply softmax to get probabilities
+    AsDiscrete(argmax=True, dtype=torch.int32),  # Convert to class labels
+    KeepLargestConnectedComponent(),        # Remove small disconnected regions
+    Invert(transform=preprocess),           # Restore original space
+    # Save the result
+    SaveImage(output_dir="./segmentations")
+])
+```
+
+    /home/suraj/miniconda3/lib/python3.10/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.croppad.array CropForeground.__init__:allow_smaller: Current default value of argument `allow_smaller=True` has been deprecated since version 1.2. It will be changed to `allow_smaller=False` in version 1.5.
+      warn_deprecated(argname, msg, warning_category)
+
+
+## Run Inference
+Process an input CT scan and generate segmentation
+
+
+```python
+# Input path
+input_path = "/home/suraj/Repositories/lighter-ct-fm/semantic-search-app/assets/scans/s0114.nii.gz"
+
+# Preprocess input
+input_tensor = preprocess(input_path)
+
+# Run inference
+with torch.no_grad():
+    output = inferer(input_tensor.unsqueeze(dim=0), model)[0]
+
+# Copy metadata from input
+output.applied_operations = input_tensor.applied_operations
+output.affine = input_tensor.affine
+
+# Postprocess and save result
+result = postprocess(output[0])
+print("✅ Segmentation completed and saved")
+```
+
+    2025-01-16 18:41:57,674 INFO image_writer.py:197 - writing: /home/suraj/Repositories/lighter-ct-fm/semantic-search-app/assets/segmentations/0/0_trans.nii.gz
+    ✅ Segmentation completed and saved
+
+
+