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Ambiguous Medical Image Segmentation using Diffusion Models

CVPR 2023

We provide the official Pytorch implementation of the paper Ambiguous Medical Image Segmentation using Diffusion Models

The implementation of diffusion model segmentation model presented in the paper is based on Diffusion Models for Implicit Image Segmentation Ensembles. The Gaussian encoders are from the Pytroch implementation of Probabilistic Unet.

Paper Abstract

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights.

Data

We evaluated our method on the LIDC dataset. For our dataloader, the expert annotations as well as the original images need to be stored in the following structure:

data
└───training
│   └───0
│       │   image_0.jpg
│       │   label0_.jpg
│       │   label1_.jpg
│       │   label2_.jpg
│       │   label3_.jpg
│   └───1
│       │  ...
└───testing
│   └───3
│       │   image_3.jpg
│       │   label0_.jpg
│       │   label1_.jpg
│       │   label2_.jpg
│       │   label3_.jpg
│   └───4
│       │  ...

An example can be seen in folder data.

Usage

We set the flags as follows:


MODEL_FLAGS="--image_size 128 --num_channels 128 --class_cond False --num_res_blocks 2 --num_heads 1 --learn_sigma True --use_scale_shift_norm False --attention_resolutions 16"
DIFFUSION_FLAGS="--diffusion_steps 1000 --noise_schedule linear --rescale_learned_sigmas False --rescale_timesteps False"
TRAIN_FLAGS="--lr 1e-4 --batch_size 20"

To train the ambiguous segmentation model, run

!CUDA_VISIBLE_DEVICES=0,4 python -m torch.distributed.launch --nproc_per_node=2 scripts/segmentation_train.py --data_dir ./data/training $TRAIN_FLAGS $MODEL_FLAGS $DIFFUSION_FLAGS

The model will be saved in the results folder. For sampling an ensemble of 4 segmentation masks with the DDPM approach, run:

python scripts/segmentation_sample.py  --data_dir ./data/testing  --model_path ./results/savedmodel.pt --num_ensemble=4 $MODEL_FLAGS $DIFFUSION_FLAGS

The generated segmentation masks will be stored in the results folder. A visualization can be done using Visdom. If you encounter high frequency noise, you can use noise filters such as median blur in post-processing step.