Condensely connected architecture for Cardiac Cine MR Image Segmentation and Quantification
This repository contains code to train state-of-the-art cardiac segmentation networks as described in this paper: CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation. This architecture is trained on MICCAI 2017 ACDC Cardiac segmentation challenge.
In this work, we propose a novel memory-efficient Convolutional Neural Network (CNN) architecture as a modification of both CondenseNet, as well as DenseNet for ventricular blood-pool segmentation by introducing a bottleneck block and an upsampling path known as CondenseUNet. Our experiments show that the proposed architecture runs on the Automated Cardiac Diagnosis Challenge (ACDC) dataset using half (50%) the memory requirement of DenseNet and one-twelfth (∼ 8%) of the memory requirements of U-Net, while still maintaining excellent accuracy of cardiac segmentation. We validated the framework on the MICCAI STACOM 2017 challenge ACDC dataset featuring one healthy and four pathology groups whose heart images were acquired throughout the cardiac cycle and achieved the mean dice scores of 96.78% (LV blood-pool), 93.46% (RV blood-pool) and 90.1% (LVMyocardium). These results are promising and promote the proposed methods as a competitive tool for cardiac image segmentation and clinical parameter estimation that has the potential to provide fast and accurate results, as needed for pre-procedural planning and / or pre-operative applications.
Our code is compatible with python 3.7 or onward.
We depend on some python packages which need to be installed by the user:
- Tensorflow
- tqdm
- SimpleITK
- sklearn
- numpy
- nibabel
- h5py
- S. M. Kamrul Hasan ([[email protected]])()
- Cristian A. Linte
For this study, we used the ACDC dataset, which is composed of short-axis cardiac cine-MR images acquired from 100 different patients divided into 5 evenly distributed subgroups according to their cardiac condition: normal- NOR, myocardial infarction- MINF, dilated cardiomyopathy- DCM, hypertrophic cardiomyopathyHCM, and abnormal right ventricle- ARV, available as a part of the STACOM 2017 ACDC challenge.
We evaluate CondenseUNet architectures for segmentation as well as clinical parameter estimation. The output of the model is a pixel-by-pixel mask that shows the class of each pixel.
Our proposed CondenseUNetframework substitutes the concept of both standard convolution and group convolution (G-Conv) with learned group-convolution (LGConv). While the standard convolution needs an increased level of computation, i.e. O(Ii x Oo), and concurrently, the pre-defined use of filters in each group convolution restricts its representation capability, these aforementioned problems are mitigated by introducing LG-Conv that learns group convolution dynamically during training through a multi-stage scheme.
Figure shows segmentation results and the ground truth masks for both 2D and 3D cases. It summarizes the comparison results, which show that our proposed model significantly improved the segmentation performance against several state-of-the-art multi-class segmentation techniques in terms of Dice metrics, Hausdorff distance, and clinical parameters. Our proposed L-CO-Net architecture achieved Dice score (Hausdorff distance) of 96.8%(7.9mm) and 95.1%(6.4mm) for the LV bloodpool, 89.5%(8.9mm) and 90.0%(8.9mm) for the LVMyocardium and 93.3%(11.2mm) and 87.43%(11.9mm) for the RV blood-pool in end-diastole and end-systole, respectively.
Quantitative evaluation of the segmentation results in terms of Mean Dice score (%) with Hausdorff distance(in mm), no. of parameters (×106 ), and the clinical indices evaluated on the ACDC dataset for LV, RV blood-pool and LV-myocardium compared across several best performing networks, including L-CONet. The statistical significance of the results for L-CO-Net model compared against five other base architectures.
open a new terminal: (forKeras) hostname@biomed:~/cardiac$ tensorboard —logdir==training:/path/to/summary/ —host=127.0.0.1 —port=6007
tensorboard —logdir==training:/home/kamrul/leon_saidur/trained_models/ACDC/SMKH_ACDC/summary/train/ —host=127.0.0.1 —port=6007
alternate approach: python -m tensorboard.main --logdir=./logs
If you find this work useful for your publications, please consider citing:
Hasan, SM Kamrul, and Cristian A. Linte. "CondenseUNet: a memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation." Medical Imaging 2020: Image-Guided Procedures, Robotic Interventions, and Modeling. Vol. 11315. International Society for Optics and Photonics, 2020.'
Kamrul Hasan, S. M., and Cristian A. Linte. "CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation." arXiv (2020): arXiv-2004.