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Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge (Part 3)

Experimental Details and Ablation Studies.

In this part, we will show the effectivness and the ablation studies of Light-weight Deformable Registration Network and Adversarial Learning Algorithm with Distilling Knowledge.


As mentioned in [1], we train method on two types of scans: Liver CT scans and Brain MRI scans.

For Liver CT scans, we use 5 datasets:

  1. LiTS contains 131 liver segmentation scans.
  2. MSD has 70 liver tumor CT scans, 443 hepatic vessels scans, and 420 pancreatic tumor scans.
  3. BFH is a smaller dataset with 92 scans.
  4. SLIVER is a challenging dataset with 20 liver segmentation scans and annotated by 3 expert doctors.
  5. LSPIG (Liver Segmentation of Pigs) contains 17 pairs of CT scans from pigs, provided by the First Affiliated Hospital of Harbin Medical University.

For Brain MRI scans, we use 4 datasets: 1. ADNI contains 66 scans. 2. ABIDE contains 1287 scans. 3. ADHD contains 949 scans. 4. LPBA has 40 scans, each featuring a segmentation ground truth of 56 anatomical structures.


We compare LDR ALDK method with the following recent deformable registration methods:

  • ANTs SyN and Elastix B-spline are methods that find an optimal transformation by iteratively update the parameters of the defined alignment.
  • VoxelMorph predicts a dense deformation in an unsupervised manner by using deconvolutional layers.
  • VTN is an end-to-end learning framework that uses convolutional neural networks to register 3D medical images, especially large displaced ones.
  • RCN is a recent recursive deep architecture that utilizes learnable cascade and performs progressive deformation for each warped image.


Table 1 summarizes the overall performance, testing speed, and the number of parameters compared with recent state-of-the-art methods in the deformable registration task. The results clearly show that Light-weight Deformable Registration network (LDR) accompanied by Adversarial Learning with Distilling Knowledge (ALDK) algorithm significantly reduces the inference time and the number of parameters during the inference phase. Moreover, the method achieves competitive accuracy with the most recent highly performed but expensive networks, such as VTN or VoxelMorph. We notice that this improvement is consistent across all experiments on different datasets SLIVER, LiTS, LSPIG, and LPBA.

In particular, we observe that on the SLIVER dataset the Dice score of best model with 3 cascades (3-cas LDR + ALDK) is 0.3% less than the best result of 3-cas VTN + Affine, while inference speed is ?21 times faster on a CPU and the parameters used during inference is ~8 times smaller. Including benchmarking results in three other datasets, i.e., LiTS, LSPIG, and LPBA, light-weight model only trades off an average of 0.5% in Dice score and 1.25% in Jacc score for a significant gain of speed and a massive reduction in the number of parameters. We also notice that method is the only work that achieves the inference time of approximately 1s on a CPU. This makes method well suitable for deployment as it does not require expensive GPU hardware for inference.



Ablation Study

Effectiveness of ALDK. Table 2 summarizes the effectiveness of Adversarial Learning with Distilling Knowledge (ALDK) when being integrated into the light-weight student network. Note that LDR without ALDK is trained using only the reconstruction loss in an unsupervised learning setup. From this table, we clearly see that ALDK algorithm improves the Dice score of the LDR tested in the SLIVER dataset by 3.4%, 4.0%, and 3.1% for 1-cas, 2-cas, and 3-cas setups, respectively. Additionally, using ALDK also increases the Jacc score by 5.2%, 4.9%, and 3.9% for 1-cas LDR, 2-cas LDR, and 3-cas LDR. These results verify the stability of adversarial learning algorithm in the inference phase, under the differences evaluation metrics, as well as the number of cascades setups. Furthermore, Table 2 also clearly shows the effectiveness and generalization of ALDK when being applied to the student network. Since the deformations extracted from the teacher are used only in the training period, adversarial learning algorithm fully maintains the speed and the number of parameters for the light-weight student network during inference. All results indicate that student network incorporated with the adversarial learning algorithm successfully achieves the performance goal, while maintaining the efficient computational cost of the light-weight setup.



Accuracy vs. Complexity. Figure 1 demonstrates the experimental results from the SLIVER dataset between LDR + ALDK and the baseline VTN under multiple recursive cascades setup on both CPU and GPU. On the CPU (Figure 1-a), in terms of the 1-cascade setup, the Dice score of method is 0.2% less than VTN while the speed is ~15 times faster. The more the number of cascades is leveraged, the higher the speed gap between LDR + ALDK and the baseline VTN, e.g. the CPU speed gap is increased to ~21 times in a 3-cascades setup. We also observe the same effect on GPU (Figure 1-b), where method achieves slightly lower accuracy results than VTN, while clearly reducing the inference time. These results indicate that LDR + ALDK can work well with the teacher network to improve the accuracy while significantly reducing the inference time on both CPU and GPU in comparison with the baseline VTN network.


Figure 1:Plots of Dice score and Inference speed with respect to the number of cascades of the baseline Affine + VTN and LDR + ALDK. (a) for CPU speed and (b) for GPU speed. Note that results are reported for the SLIVER dataset; bars represent the CPU speed; lines represent the Dice score. All methods use an Intel Xeon E5-2690 v4 CPU and Nvidia GeForce GTX 1080 Ti GPU for inference.


Figure 2 illustrates the visual comparison among 1-cas LDR, 1-cas LDR + ALDK, and the baseline 1-cas RCN. Five different moving images in a volume are selected to apply the registration to a chosen fixed image. It is important to note that though the sections of the warped segmentations can be less overlap with those of the fixed one, the segmentation intersection over union is computed for the volume and not the sections. In the segmented images in Figure 2, besides the matched area colored by white, we also marked the miss-matched areas by red for an easy-to-read purpose.

From Figure 2, we can see that the segmentation resutls of 1-cas LDR network without using ALDK (Figure 2-a) contains many miss-matched areas (denoted in red color). However, when we apply ALDK to the student network, the registration results are clearly improved (Figure 2-b). Overall, LDR + ALDK visualization results in Figure 2-b are competitive with the baseline RCN network (Figure 2-c). This visualization confirms that framework for deformable registration can achieve comparable results with the recent RCN network.


Figure 2:The visualization comparison between LDR (a), LDR + ALDK (b), and the baseline RCN (c). The left images are sections of the warped images; the right images are sections of the warped segmentation (white color represents the matched areas between warped image and fixed image, red color denotes the miss-matched areas). The segmentation visualization indicates that LDR + ALDK (b) method reduces the miss-matched areas of the student network LDR (a) significantly. Best viewed in color.


[1] Tran, Minh Q., et al. "Light-weight deformable registration using adversarial learning with distilling knowledge." IEEE Transactions on Medical Imaging, 2022.

Open Source

🐱 Github: https://github.com/aioz-ai/LDR_ALDK

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