SAMIHS Intracranial Hemorrhage Segmentation

This repository packages the SAMIHS intracranial hemorrhage segmentation model used in the Medical project. It provides a simple interface:

3D brain CT NIfTI -> binary hemorrhage mask NIfTI

The model is a 2D slice model. It processes each slice independently and stacks the predicted slices back into a 3D NIfTI mask.

Files

Path	Description
`segment_ich.py`	CLI/API wrapper for one 3D NIfTI input.
`weights/SAMIHS_09170527_2_0.483.pth`	Model checkpoint.
`samihs_src/models`, `samihs_src/utils`	Minimal SAMIHS source needed for inference.
`INPUT_SPEC.md`	Input format, intensity, and orientation requirements.
`requirements.txt`	Python dependencies.

Installation

git clone https://huggingface.co/<ORG_OR_USER>/<REPO_NAME>
cd <REPO_NAME>
pip install -r requirements.txt

Use a PyTorch/CUDA environment compatible with your GPU. In the original project this was tested with:

/data/wxh/miniconda3/envs/flowmatching/bin/python
torch 2.8.0+cu128

Usage

python segment_ich.py \
  --input /path/to/brain_ct.nii.gz \
  --output /path/to/brain_ct_ich_mask.nii.gz \
  --device cuda:0 \
  --batch-size 8 \
  --threshold 0.5

Output is a uint8 NIfTI with the same shape and affine as the input. Values are 0 and 1.

Python API

from segment_ich import segment_nii

metadata = segment_nii(
    input_nii="/path/to/brain_ct.nii.gz",
    output_nii="/path/to/brain_ct_ich_mask.nii.gz",
    device="cuda:0",
    batch_size=8,
    threshold=0.5,
)
print(metadata)

Input Requirements

Read INPUT_SPEC.md before using the model. Key points:

Input must be a single 3D NIfTI file, .nii or .nii.gz.
DICOM folders, 4D NIfTI, and multi-channel images are not supported directly.
The wrapper does not reorient based on NIfTI affine; it uses stored voxel array order.
Default --slice-axis auto uses the smallest dimension as the slice/depth axis. Set --slice-axis 0/1/2 explicitly if needed.
Internally each slice is clipped to [0.5, 99.5] percentile and min-max normalized to [0, 1] before inference.

Default Parameters

encoder_input_size = 1024
batch_size = 8
threshold = 0.5
slice_axis = auto
amp = true
rotate_for_samihs = true

If CUDA OOM occurs, reduce --batch-size to 4, 2, or 1.

Validation

This packaged wrapper was checked against the existing project SAMIHS inference output on a 256x256x32 sample. The wrapper output matched the previous mask exactly, with zero differing voxels.

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