OncoReg: Medical Image Registration for Oncological Challenges

Wiebke Heyer; Yannic Elser; Lennart Berkel; Xinrui Song; Xuanang Xu; Pingkun Yan; Xi Jia; Jinming Duan

eess.IVMar 29, 2025

OncoReg: Medical Image Registration for Oncological Challenges

Authors: Wiebke Heyer, Yannic Elser, Lennart Berkel, Xinrui Song, Xuanang Xu, Pingkun Yan, Xi Jia, Jinming Duan,

Organizations: Institute of Medical Informatics, University of Lübeck, Lübeck, Germany · Institute of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany · Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USA · School of Computer Science, University of Birmingham, Birmingham, UK · Division of Informatics, Imaging and Data Sciences, University of Manchester, UK · DAMO Academy, Alibaba Group, Hangzhou, China · Hangzhou Shengshi Technology Co., Ltd, Hangzhou, China · Department of Radiation Oncology, University Hospital Schleswig-Holstein, Lübeck, Germany · EchoScout GmbH, Lübeck, Germany · Radboud University Medical Center, Nijmegen, Netherlands · Institute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany

Abstract

In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue by enabling researchers to develop and validate image registration methods through a two-phase framework that ensures patient privacy while fostering the development of more generalisable AI models. Phase one involves working with a publicly available dataset, while phase two focuses on training models on a private dataset within secure hospital networks. OncoReg builds upon the foundation established by the Learn2Reg Challenge by incorporating the registration of interventional cone-beam computed tomography with standard planning fan-beam CT images in radiotherapy. Accurate image registration is crucial in oncology, particularly for dynamic treatment adjustments in image-guided radiotherapy, where precise alignment is necessary to minimise radiation exposure to healthy tissues while effectively targeting tumours. This work details the methodology and data behind the OncoReg Challenge and provides a comprehensive analysis of the competition entries and results. Findings reveal that feature extraction plays a pivotal role in this registration task. A new method emerging from this challenge demonstrated its versatility, while established approaches continue to perform comparably to newer techniques. Both deep learning and classical approaches still play significant roles in image registration, with the combination of methods, particularly in feature extraction, proving most effective.

Figures & tables

Dataset	ThoraxCBCT	OncoReg
Modality	Fan-beam CT & Cone-beam CT	Fan-beam CT & Cone-beam CT
4D (max. Insp./Exp.)	✓	$\times$
# Cases (2 Pairs/Case)	19 (1xFBCT, 2xCBCT per Case)	27 (1xFBCT, 2xCBCT per Case)
Split	11 Train, 3 Val., 5 Test	3-Fold CV (15 Train, 3 Val., 9 Test)
Image Size	390, 280, 300	256, 192, 192
Spacing	1 mm isotropic	1.5 mm isotropic

Table 1: Overview of the two datasets for the ThoraxCBCT and OncoReg tasks.

Figure 1: Exemplary image pair from the ThoraxCBCT dataset including semi-automatic label mask annotations. Left: Fan-Beam CT. Right: Cone-Beam CT.

Figure 2: Semi-automatic label mask annotations for an exemplary Fan-Beam CT from the OncoReg dataset. 3D rendering generated with 3D Slicer ( Fedorov et al., 2012 ).

Metric	Description	Purpose	Unit
DSC	Dice Similarity Coefficient	Overlap between segmentations	%
DSC30	30th Percentile of DSC	Measures robustness of overlap	%
HD95	95th Percentile Hausdorff Dist.	Robust surface distance	mm
TRE	Target Registration Error	Landmark misalignment	mm
TRE30	70th Percentile of TRE	Robustness of landmark error	mm
SDlogJ	Standard Deviation of log(Jac.)	Local volume changes	-

Table 2: Summary of Metrics used throughout the challenge for evaluation of registration performance.

Figure 3: SynDeeds: Fixed and moving images are registered using Deeds after the fixed image went through the image restoration module.

Figure 4: Overview of the image restoration module.

Figure 5: Exemplary ThoraxCBCT image pair before registration (left: moving FBCT; middle: fixed CBCT) and after registration with ConvexAdam (right: warped FBCT).

	TRE $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	TRE30 $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	DSC $\uparrow$ ${\color[rgb]{1,1,1}a}_{\%}$	HD95 $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	SDlogJ $\downarrow$	Rank $\uparrow$
Initial	11.91	16.26	29.04	48.13
ConvexAdam	4.93	4.98	68.40	38.00	0.10	0.710
SynDeeds	6.01	5.67	70.16	22.97	0.15	0.705
NiftyReg	4.90	4.51	62.11	43.41	0.05	0.577
DeedsBCV	8.76	7.38	69.00	22.38	0.16	0.537
Fourier-Net	4.03	3.85	61.91	47.80	0.07	0.535

Table 3: Quantitative results of the ThoraxCBCT task.

Figure 6: ThoraxCBCT: Dice coefficients for each anatomical label and registration method, averaged across all test images.

	TRE $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	TRE30 $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	DSC $\uparrow$ ${\color[rgb]{1,1,1}a}_{\%}$	HD95 $\downarrow$ ${\color[rgb]{1,1,1}a}_{mm}$	SDlogJ $\downarrow$	RT $\downarrow$ ${\color[rgb]{1,1,1}a}_{s}$	Rank $\uparrow$
Initial	6.41	11.43	43.38	32.68
TimH	5.30	6.31	63.21	6.92	0.039	N/A	0.73
DINO-Reg Ens.	3.94	7.63	63.89	31.12	0.062	$>300$	0.67
VoxelMorph++	4.66	8.45	62.36	26.86	0.081	$<60$	0.60
ConvexAdam	4.16	7.89	60.13	31.03	0.066	$(<5)$	0.60
DINO-Reg	4.53	8.46	57.64	81.83	0.039	$>300$	0.57

Table 4: Quantitative Results of the OncoReg task. Runtime measurements were only recorded for submissions evaluated through the original challenge pipeline. For methods submitted after the official challenge evaluation, runtime information is not available.

Figure 9: OncoReg: Dice coefficients for ten anatomical labels and each registration method, averaged across all images.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 10: OncoReg: Relationship between mean Target Registration Error and mean Dice Similarity Coefficient across all registration methods. Each point represents one method, with TRE averaged across all landmark correspondences and DSC averaged across all images and anatomical structures. A moderate negative correlation (Spearman $\rho=-0.52$ ) is observed.

Figure 11: OncoReg: Relationship between Dice Similarity Coefficient and 95th percentile Hausdorff Distance for five clinically relevant anatomical structures across all images and registration methods.

Figure 12: OncoReg: Dice coefficients for individual vertebrae-labels and each registration method, averaged across all images.

Figure 13: OncoReg: Dice coefficients for individual rib-labels and each registration method, averaged across all images.

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CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration

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Insitute of Medical Informatics, University of Luebeck, Luebeck, Germany

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Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to the robustness of registration methods. Recent approaches leveraging self-supervised representation learning show promise by pre-training feature extractors to generate robust anatomical embeddings, that farther used for the registration. In this work, we propose a novel framework that integrates equivariant contrastive learning directly into the registration model. Our approach leverages the power of contrastive learning to learn robust feature representations that are invariant to tissue deformations. By jointly optimizing the contrastive and registration objectives, we ensure that the learned representations are not only informative but also suitable for the registration task. We evaluate our method on abdominal and thoracic image registration tasks, including both intra-patient and inter-patient scenarios. Experimental results demonstrate that the integration of contrastive learning directly into the registration framework significantly improves performance, surpassing strong baseline methods.

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