• EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography (MICCAI-2021)
• • Framework
  • Installation
  • Experiments
  • Training and Evaluation
  • Reproduction
  • License
  • Citation
  • Contact

EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography (MICCAI-2021)

by Jiancong Chen, Yingying Zhang, Jingyi Wang, Xiaoxue Zhou, Yihua He*, Tong Zhang*.

This repo contains the official pytorch implemetation for EllipseNet.

Framework

Installation

Please refer to INSTALL.md for installation instructions.

Experiments

Methods	Setting	DiceT	DiceC	Diceall	Pavg
EllipseNet (exp6)	only IoU loss	0.8813	0.8520	0.8666	0.8855
EllipseNet (exp1)	w/o IoU loss	0.9338	0.9108	0.9224	0.8841
EllipseNet (exp3)	w/ IoU loss	0.9430	0.9224	0.9336	0.8949

Training and Evaluation

Prepare the elliptical dataset in coco-format. An example script is given in scripts/prepare_label.ipynb. We provide scripts for all the experiments in the experiments folder.

Usage:

chmod +x experiments/miccai21/*.sh
./experiments/miccai21/exp3_base_theta5_iou.sh

Reproduction

If you need the docker for reproduction, please contact via email. We will provide the docker image.

License

EllipseNet itself is released under the MIT License (refer to the LICENSE file for details).
Portions of the code are borrowed from CenterNet, Rotated_IoU, human-pose-estimation.pytorch (image transform, resnet), CornerNet (hourglassnet, loss functions), dla (DLA network), DCNv2 (deformable convolutions), tf-faster-rcnn (Pascal VOC evaluation) and kitti_eval (KITTI dataset evaluation). Please refer to the original License of these projects (See NOTICE).

Citation

If you find this project useful for your research, please use the following BibTeX entry.

Chen J., Zhang Y., Wang J., Zhou X., He Y., Zhang T*. (2021) EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography. In: de Bruijne M. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science, vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_21

Contact

If you have any questions about this paper, welcome to email to zhangt02@pcl.ac.cn