BiomedParse

This repository hosts the code and resources for the paper "A Foundation Model for Joint Segmentation, Detection, and Recognition of Biomedical Objects Across Nine Modalities" (published in Nature Methods).

[Paper] [Demo] [Model] [Data] [BibTeX]

BiomedParse is designed for comprehensive biomedical image analysis. It offers a unified approach to perform segmentation, detection, and recognition across diverse biomedical imaging modalities. By consolidating these tasks, BiomedParse provides an efficient and flexible tool tailored for researchers and practitioners, facilitating the interpretation and analysis of complex biomedical data.

News

• Nov. 22, 2024: We added negative prediction p-value example in inference_example_DICOM.ipynb
• Nov. 18, 2024: BiomedParse is officially online in Nature Methods!

Installation

git clone https://github.com/microsoft/BiomedParse.git

Conda Environment Setup

Option 1: Directly build the conda environment

Under the project directory, run

conda env create -f environment.yml

Option 2: Create a new conda environment from scratch

conda create -n biomedparse python=3.9.19
conda activate biomedparse

Install Pytorch

conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch -c nvidia

In case there is issue with detectron2 installation, make sure your pytorch version is compatible with CUDA version on your machine at https://pytorch.org/.

Install dependencies

pip install -r assets/requirements/requirements.txt

Dataset

BiomedParseData was created from preprocessing publicly available biomedical image segmentation datasets. Check a subset of our processed datasets on HuggingFace: https://huggingface.co/datasets/microsoft/BiomedParseData. For the source datasets, please check the details here: BiomedParseData. As a quick start, we've samples a tiny demo dataset at biomedparse_datasets/BiomedParseData-Demo

Model Checkpoints

We host our model checkpoints on HuggingFace here: https://huggingface.co/microsoft/BiomedParse.

Step 1. Create pretrained model folder

mkdir pretrained

Step 2. Download model checkpoint and put the model in the pretrained folder when running the code. Change file name to biomed_parse.pt

Expect future updates of the model as we are making it more robust and powerful based on feedbacks from the community. We recomment using the latest version of the model.

Running Inference with BiomedParse

We’ve streamlined the process for running inference using BiomedParse. Below are details and resources to help you get started.

How to Run Inference

To perform inference with BiomedParse, use the provided example code and resources:

• Inference Code: Use the example inference script in example_prediction.py.
• Sample Images: Load and test with the provided example images located in the examples directory.
• Model Configuration: The model settings are defined in configs/biomedparse_inference.yaml.

Example Notebooks

We’ve included sample notebooks to guide you through running inference with BiomedParse:

• DICOM Inference Example: Check out the inference_examples_DICOM.ipynb notebook for example using DICOM images.
• You can also try a quick online demo:

Model Setup

from PIL import Image
import torch
from modeling.BaseModel import BaseModel
from modeling import build_model
from utilities.distributed import init_distributed
from utilities.arguments import load_opt_from_config_files
from utilities.constants import BIOMED_CLASSES
from inference_utils.inference import interactive_infer_image
import numpy as np

# Build model config
opt = load_opt_from_config_files(["configs/biomedparse_inference.yaml"])
opt = init_distributed(opt)

# Load model from pretrained weights
#pretrained_pth = 'pretrained/biomed_parse.pt'
pretrained_pth = 'hf_hub:microsoft/BiomedParse'

model = BaseModel(opt, build_model(opt)).from_pretrained(pretrained_pth).eval().cuda()
with torch.no_grad():
    model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(BIOMED_CLASSES + ["background"], is_eval=True)

Segmentation On Example Images

# RGB image input of shape (H, W, 3). Currently only batch size 1 is supported.
image = Image.open('examples/Part_1_516_pathology_breast.png', formats=['png'])
image = image.convert('RGB')
# text prompts querying objects in the image. Multiple ones can be provided.
prompts = ['neoplastic cells', 'inflammatory cells']

# load ground truth mask
gt_masks = []
for prompt in prompts:
    gt_mask = Image.open(f"examples/Part_1_516_pathology_breast_{prompt.replace(' ', '+')}.png", formats=['png'])
    gt_mask = 1*(np.array(gt_mask.convert('RGB'))[:,:,0] > 0)
    gt_masks.append(gt_mask)

pred_mask = interactive_infer_image(model, image, prompts)

# prediction with ground truth mask
for i, pred in enumerate(pred_mask):
    gt = gt_masks[i]
    dice = (1*(pred>0.5) & gt).sum() * 2.0 / (1*(pred>0.5).sum() + gt.sum())
    print(f'Dice score for {prompts[i]}: {dice:.4f}')

Detection and recognition inference code are provided in inference_utils/output_processing.py.

• check_mask_stats(): Outputs p-value for model-predicted mask for detection.
• combine_masks(): Combines predictions for non-overlapping masks.

Finetune on Your Own Data

While BiomedParse can take in arbitrary image and text prompt, it can only reasonably segment the targets that it has learned during pretraining! If you have a specific segmentation task that the latest checkpint doesn't do well, here is the instruction on how to finetune it on your own data.

Raw Image and Annotation

BiomedParse expects images and ground truth masks in 1024x1024 PNG format. For each dataset, put the raw image and mask files in the following format

├── biomedparse_datasets
    ├── YOUR_DATASET_NAME
        ├── train
        ├── train_mask
        ├── test
        └── test_mask

Each folder should contain .png files. The mask files should be binary images where pixels != 0 indicates the foreground region.

File Name Convention

Each file name follows certain convention as

[IMAGE-NAME]_[MODALITY]_[SITE].png

[IMAGE-NAME] is any string that is unique for one image. The format can be anything. [MODALITY] is a string for the modality, such as "X-Ray" [SITE] is the anatomic site for the image, such as "chest"

One image can be associated with multiple masks corresponding to multiple targets in the image. The mask file name convention is

[IMAGE-NAME]_[MODALITY]_[SITE]_[TARGET].png

[IMAGE-NAME], [MODALITY], and [SITE] are the same with the image file name. [TARGET] is the name of the target with spaces replaced by '+'. E.g. "tube" or "chest+tube". Make sure "_" doesn't appear in [TARGET].

Get Final Data File with Text Prompts

In biomedparse_datasets/create-customer-datasets.py, specify YOUR_DATASET_NAME. Run the script with

cd biomedparse_datasets
python create-customer-datasets.py

After that, the dataset folder should be of the following format

├── dataset_name
        ├── train
        ├── train_mask
        ├── train.json
        ├── test
        ├── test_mask
        └── test.json

Register Your Dataset for Training and Evaluation

In datasets/registration/register_biomed_datasets.py, simply add YOUR_DATASET_NAME to the datasets list. Registered datasets are ready to be added to the training and evaluation config file configs/biomed_seg_lang_v1.yaml. Your training dataset is registered as biomed_YOUR_DATASET_NAME_train, and your test dataset is biomed_YOUR_DATASET_NAME_test.

Train BiomedParse

To train the BiomedParse model, run:

bash assets/scripts/train.sh

This will continue train the model using the training datasets you specified in configs/biomed_seg_lang_v1.yaml

Evaluate BiomedParse

To evaluate the model, run:

bash assets/scripts/eval.sh

This will continue evaluate the model on the test datasets you specified in configs/biomed_seg_lang_v1.yaml. We put BiomedParseData-Demo as the default. You can add any other datasets in the list.

Citation

Please cite our paper if you use the code, model, or data.

@article{zhao2024biomedparse,
  title = {A foundation model for joint segmentation, detection, and recognition of biomedical objects across nine modalities},
  author = {Zhao, Theodore and Gu, Yu and Yang, Jianwei and Usuyama, Naoto and Lee, Ho Hin and Kiblawi, Sid and Naumann, Tristan and Gao, Jianfeng and Crabtree, Angela and Abel, Jacob and Moung-Wen, Christine and Piening, Brian and Bifulco, Carlo and Wei, Mu and Poon, Hoifung and Wang, Sheng},
  journal = {Nature Methods},
  year = {2024},
  publisher = {Nature Publishing Group UK London},
  url = {https://www.nature.com/articles/s41592-024-02499-w},
  doi = {10.1038/s41592-024-02499-w}
}

Usage and License Notices

The model described in this repository is provided for research and development use only. The model is not intended for use in clinical decision-making or for any other clinical use, and the performance of the model for clinical use has not been established. You bear sole responsibility for any use of this model, including incorporation into any product intended for clinical use.