medsam-lite-tools 0.1.1

LiteMedSAM segmentation training and inference tools.

medsam-lite-tools

medsam-lite-tools is a LiteMedSAM segmentation package for model building, fine-tuning, inference, and Python-based experimentation.

It provides:

• a LiteMedSAM model builder with pretrained initialization
• a command-line training loop for preprocessed .npz segmentation data
• a command-line inference runner for .npz image samples
• Python APIs for training and prediction workflows

Install

pip install medsam-lite-tools

For training workflows with common medical-image utilities:

pip install "medsam-lite-tools[train]"

Build a Model

from medsam_lite_tools import model

net = model(pretrained=True, root="./temp/")

pretrained=True builds the LiteMedSAM architecture with pretrained initialization and is the default starting point for fine-tuning. Use pretrained=False when training from scratch.

from medsam_lite_tools import model

net = model(pretrained=False)

Training

Training input is a directory of .npz files.

Each file should include:

• image, img, data, or x: image array
• mask, label, seg, gt, or y: binary mask array
• optional box or bbox: [x_min, y_min, x_max, y_max]

If no box is provided, the trainer computes one from the mask.

medsam-lite-train ^
  --data-dir .\train_npz ^
  --output-dir .\runs\medsam_lite ^
  --epochs 5 ^
  --batch-size 2 ^
  --lr 1e-5 ^
  --checkpoint-root .\temp

The command starts from the pretrained LiteMedSAM initialization by default and writes latest.pt to the output directory.

The same workflow can be called from Python:

from medsam_lite_tools import fit

net, history = fit(
    data_dir="./train_npz",
    output_dir="./runs/medsam_lite",
    epochs=5,
    batch_size=2,
    lr=1e-5,
    checkpoint_root="./temp",
)

Inference

Inference input is a directory of .npz image samples. Each file should include image, img, data, or x. If box or bbox is present, it is used as the prompt box. Otherwise, the full 256 x 256 image area is used.

medsam-lite-infer ^
  --input-dir .\test_npz ^
  --output-dir .\runs\medsam_lite_predictions ^
  --checkpoint .\runs\medsam_lite\latest.pt ^
  --root .\temp

The command writes one compressed .npz mask file per input sample.

Python usage:

from medsam_lite_tools import model, predict_npz_dir

net = model(pretrained=True, root="./temp/")
outputs = predict_npz_dir(
    input_dir="./test_npz",
    output_dir="./runs/medsam_lite_predictions",
    checkpoint="./runs/medsam_lite/latest.pt",
    root="./temp",
)
print(outputs)

Notes

• The training and inference APIs use LiteMedSAM pretrained initialization when pretrained=True.

Attribution

The model architecture and initialization workflow are based on the LiteMedSAM branch of bowang-lab/MedSAM.

• Upstream repository: https://github.com/bowang-lab/MedSAM/tree/LiteMedSAM
• Upstream license: Apache License 2.0

The vendored code is limited to the LiteMedSAM model construction path needed for training and inference experiments.