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DeepLesion Codebase for Universal Lesion Detection

This repository contains the codebase for our 2 papers A3D (MICCAI'21) and AlignShift (MICCAI'20), which achieves state-of-the-art performance on DeepLesion for universal lesion detection.

  • Asymmetric 3D Context Fusion for Universal Lesion Detection (MICCAI'21)

  • AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes (MICCAI'20, early accepted)

Code structure

  • nn The core implementation of AlignShift convolution and TSM convolution, including the operators, models, and 2D-to-3D/AlignShift/TSM model converters.
    • operators: A3DConv, AlignShiftConv, TSMConv.
    • converters.py: include converters which convert 2D models to 3DConv/AlignShiftConv/TSMConv/A3DConv counterparts.
    • models: Native AlignShift/TSM/A3DConv models.
  • deeplesion The experiment code is based on mmdetection, this directory consists of compounents used in mmdetection.
  • mmdet: a duplication of mmdetection with our new models registered.

Installation

  • git clone this repository
  • pip install -e .

The code requires only common Python environments for machine learning. Basically, it was tested with Python 3 (>=3.6) PyTorch==1.3.1 numpy==1.18.5, pandas==0.25.3, scikit-learn==0.22.2, Pillow==8.0.1, fire, scikit-image Higher (or lower) versions should also work (perhaps with minor modifications).

Convert a 2D model into 3D with a single line of code

from converter import Converter
import torchvision
from alignshift import AlignShiftConv
# m is a standard pytorch model
m = torchvision.models.resnet18(True)
alignshift_conv_cfg = dict(conv_type=AlignShiftConv, 
                          n_fold=8, 
                          alignshift=True, 
                          inplace=True,
                          ref_spacing=0.2, 
                          shift_padding_zero=True)
m = Converter(m, 
              alignshift_conv_cfg, 
              additional_forward_fts=['thickness'], 
              skip_first_conv=True, 
              first_conv_input_channles=1)
# after converted, m is using AlignShiftConv and capable of processing 3D volumes
x = torch.rand(batch_size, in_channels, D, H, W)
thickness = torch.rand(batch_size, 1)
out = m(x, thickness)

Usage of AlignShiftConv/TSMConv/A3DConv operators

from nn.operators import AlignShiftConv, TSMConv, A3DConv
x = torch.rand(batch_size, 3, D, H, W)
thickness = torch.rand(batch_size, 1)
# AlignShiftConv to process 3D volumnes
conv = AlignShiftConv(in_channels=3, out_channels=10, kernel_size=3, padding=1, n_fold=8, alignshift=True, ref_thickness=2.0)
out = conv(x, thickness)
# TSMConv to process 3D volumnes
conv = TSMConv(in_channels=3, out_channels=10, kernel_size=3, padding=1, n_fold=8, tsm=True)
out = conv(x)
# A3DConv to process 3D volumnes
conv = A3DConv(in_channels=3, out_channels=10, kernel_size=3, padding=1, dimension=3)
out = conv(x)

Usage of native AlignShiftConv/TSMConv models

from nn.models import DenseNetCustomTrunc3dAlign, DenseNetCustomTrunc3dTSM
net = DenseNetCustomTrunc3dAlign(num_classes=3)
B, C_in, D, H, W = (1, 3, 7, 256, 256)
input_3d = torch.rand(B, C_in, D, H, W)
thickness = torch.rand(batch_size, 1)
output_3d = net(input_3d, thickness)

How to run the experiments

  • Dataset

  • Preparing mmdetection script

  • Model weights Our trained weights available on:

  • Training

    ./deeplesion/train_dist.sh ${mmdetection script} ${dist training GPUS}
    • Train AlignShiftConv models
    ./deeplesion/train_dist.sh ./deeplesion/mconfig/densenet_align.py 2
    • Train TSMConv models
    ./deeplesion/train_dist.sh ./deeplesion/mconfig/densenet_tsm.py 2
    • Train A3DConv models
    ./deeplesion/train_dist.sh ./deeplesion/mconfig/densenet_a3d.py 2

  • Evaluation

    ./deeplesion/eval.sh ${mmdetection script} ${checkpoint path}
    ./deeplesion/eval.sh ./deeplesion/mconfig/densenet_align.py ./deeplesion/model_weights/alignshift_7slice.pth

Citation

If you find this project useful, please cite the following papers:

Jiancheng Yang, Yi He, Kaiming Kuang, Zudi Lin, Hanspeter Pfister, Bingbing Ni. "Asymmetric 3D Context Fusion for Universal Lesion Detection". International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021.

Jiancheng Yang, Yi He, Xiaoyang Huang, Jingwei Xu, Xiaodan Ye, Guangyu Tao, Bingbing Ni. "AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes". International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020.

or using bibtex:

@inproceedings{yang2021asymmetric,
  title={Asymmetric 3D Context Fusion for Universal Lesion Detection},
  author={Yang, Jiancheng and He, Yi and Kuang, Kaiming and Lin, Zudi and Pfister, Hanspeter and Ni, Bingbing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  pages={571--580},
  year={2021},
  organization={Springer}
}

@inproceedings{yang2020alignshift,
  title={AlignShift: bridging the gap of imaging thickness in 3D anisotropic volumes},
  author={Yang, Jiancheng and He, Yi and Huang, Xiaoyang and Xu, Jingwei and Ye, Xiaodan and Tao, Guangyu and Ni, Bingbing},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)},
  pages={562--572},
  year={2020},
  organization={Springer}
}