Title: Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges
Venue: NeurIPS 2025
Accepted: yes
Keywords: Schrödinger Bridge

Harmonization

Optical Coherence Tomography

Anatomy

Metric
Abstract: Medical image harmonization aims to reduce the differences in appearance caused by scanner hardware variations to allow for consistent and reliable comparisons across devices. Harmonization based on paired images from different devices has limited applicability in real-world clinical settings. On the other hand, unpaired harmonization typically does not guarantee anatomy consistency, which is problematic because anatomical information preservation is paramount. The Schrödinger bridge framework has achieved state-of-the-art style transfer performance with natural images by matching distributions of unpaired images, but this approach can also introduce anatomy changes when applied to medical images. We show that such changes occur because the Schrödinger bridge uses the square of the Euclidean distance between images as the transport cost in an entropy-regularized optimal transport problem. Such a transport cost is not appropriate for measuring anatomical distances, as medical images with the same anatomy need not have a small Euclidean distance between them. In this paper, we propose a latent metric Schrödinger bridge (LMSB) framework to improve the anatomical consistency for the harmonization of medical images. We develop an invertible network that maps medical images into a latent Euclidean metric space where the distances among images with the same anatomy are minimized using the pullback latent metric. Within this latent space, we train a Schrödinger bridge to match distributions. We show that the proposed LMSB is superior to the direct application of a Schrödinger bridge to harmonize optical coherence tomography (OCT) images.


Peer reviews:
comments: SUMMARY:
The paper introduces a new method, the latent metric Schrödinger bridge (LMSB), which improves anatomical consistency for the harmonisation of medical images. They train an invertible network that maps the OCT images into a latent Euclidean space where distances between images with the same anatomical structure are minimized, The authors show that the method was able to harmonise OCT images.

STRENGTHS AND WEAKNESSES:
Strengths:
- Comparison to other methods
- The paper tackles the challenge of domain shift caused by different scanners or acquisition parameters

Weaknesses:
- There is a lack of statistical significance that supports the main claim of the paper.

QUESTIONS:
1. Could the authors elaborate on how the method would apply to other medical images? The paper discusses the case of OCT images. Would there be any problems in applying it to MRI or Ultrasounds? Would the variability of the acquisition of ultrasound make it harder to apply the proposed method ? In the limitations section, the authors mention that the complexity of the anatomy might add complexity to the method. Can the authors elaborate on that point?
2. Could the authors provide a more thorough statistical analysis of the quantitative results presented?
3. Can the authors expand on what computational resources were used in this work?
4. Line 54: please elaborate which assumptions from OT and SB are not correct for medical image harmonization.
5. The authors report the MAE as the evaluation metric, could the authors clarify why this is the only metric reported instead of FID and more traditional generative metrics?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper develops a method, namely anatomy-guided latent metric Schrödinger bridge (LMSB), for OCT image harmonization to reduce anatomical shifts. The authors provide theoretical, quantitative, and qualitative analysis of the proposed algorithm on a private dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed LMSB framework enhances anatomical consistency in medical image harmonization by mapping images to a learned latent Euclidean space using an invertible neural network.\n2. The authors provide a rigorous theoretical analysis of why conventional Euclidean transport costs in SB and optimal transport are unsuitable for medical images.\n\nWeakness: \n1. The method is only demonstrated on retinal OCT images, which have relatively simple and regular anatomical structures.\n2. There is no evidence that LMSB can generalize to other imaging modalities, or even for other OCT vendors. For example, the two OCT systems in the paper (Zeiss Cirrus OCT and Heidelberg Spectralis OCT ) are SDOCT systems. Will the algorithm be effective between SDOCT and SSOCT?\n3. The authors claim that the proposed algorithm is for the image harmonization task. However, the evaluation metrics include MAE and LNCC. Can these two metrics measure the domain consistency between two modalities?\n\nQUESTIONS:\n1. Will the pre-processing of the images affect the performance of the algorithm? What is the original size of the OCT images?\n2. The authors may consider adding more evaluation metrics, such as PSNR, SSIM or some Edge-preserving metrics, to better demonstrate the performance of the algorithm. Even though the pairs are not pixel-wise matched, the authors may consider using registration algorithms before calculating these metrics.\n3. The authors may consider collecting feedback from the experts to validate the performance of the algorithm.\n\nLIMITATIONS:\nThe authors may consider discussing the limitations of the societal impacts, not just the technical impacts.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-01"}

comments: SUMMARY:
Existing image harmonization approaches distort the underlying anatomy (often severely in my experience.) This approach uses the existing annotations of anatomy to suppress this issue in the framework of a Diffusion Schrödinger Bridge. The result is a meaningful improvement in faithfulness.

STRENGTHS AND WEAKNESSES:
There is a great deal of math going on, but digging to the root of it it appears that the latent variable z is just the segmentations associated with the data, and so the correspondence that is being preserved is just that the segmentation shouldn't change. This is admirable and of course the goal, but it could have been stated more clearly. The specific way of using segmentations as a flow matching target is clever and well integrated to the schroedinger bridge framework.

On the other side of clarity, Figure 1 is excellently done, and was central to my ability to grok the paper's mathematical content.

QUESTIONS:
The paper states that the dataset consisted of 16500 slices from each machine, coming from ~330 scans, and split into train and test set at a ration of 15000 slices to 1500 slices. Please confirm whether the train-test split was done at the scan level or the slice level- the phrasing makes it sound concerningly like the split was done at the slice level, which would introduce heavy test set contamination.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the long-standing “anatomy-shift” problem that occurs when applying Schrödinger Bridge (SB) or entropy-regularized optimal transport directly to medical images. In this manuscript, the authors proposed anatomy guided latent metric Schrödinger Bridge (LMSB) to achieve optical coherence tomography (OCT) image harmonization. The authors demonstrated that directly using SB might cause anatomy shift, which could affect the downstream tasks. The main technical contributions is mapping OCT images to a latent Euclidean metric space via anatomy guidance, which was demonstrated to have better performance than DDIB and DSBM.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper formalizes how pixel-space Euclidean costs confound contrast variation with anatomical differences.\n2. Training a network to “flatten” anatomy into a Euclidean metric, followed by SB in that space, is conceptually elegant and empirically effective.\n3. The proposed method does not require paired data, which would be very helpful in the field of medical imaging.\n4. Compared to other pipelines, the proposed method displayed expecptional improved. And there's almost no anatomy shift for images generated by LMSB ODE.\n\nWeaknesses:\n\n1. I have a concern about the dataset. For the dataset preparation, the authors acquired 16, 500 Cirrus B-scans sampled from 387 Cirrus OCT volumes, and 16, 500 Spectralis B-scans sampled from 337 Spectralis OCT volumes. And then, they split the B-scans into 15,000 and 1,500 for each OCT device. The field of view of OCT is quite small (usually 3x3, 6x6 or 12x12 mm2) compared to other imaging modalities like ultrasound and CT. Consequently, the B-scans within a single volume are often highly similar, especially those that are adjacent or otherwise close to one another. Hence, I feel like splitting the dataset by B-scans rather than the volumes is a kind of data leakage and cannot reflect the performance in the 'real world' setting.\n2. The current validation is insufficient. I suggest acquiring scans of the same retinal region in a single participant with both OCT devices and then registering the two volumes (both two devices mentioned in the manuscript can automately take OCT images located the central fovea of the retina, so the subsquent registration is not quite hard). This setup would enable a direct, voxel-wise comparison between the synthesized and ground-truth images, clearly revealing any discrepancies.\n3. This works relies on deep learning segementation of the retina layers, so any bias or error in the segmentation could propagate and adversely affect the subsequent LMSB stage.\n4. For the inner retina, the current OCT/ophthalmology studies focus a lot on RNFL-GCL as it's related to glucoma (biomaker: thickness of the layer) or other nuerodegenration diseases (biomarker: morphology of the retina ganglion axon bundles). Unfortunately, the proposed method does not deliver satisfactory performance on this critical layer.\n\nQUESTIONS:\n1. Why did authors choose segmentation label maps as the sole anatomy-preserving coordinate? Have them explored alternative learned metrics.\n2. Can author address my concern on the dataset preparation?\n3. Can author acquire scans of the same retinal region in a single participant with both OCT devices and display the harmonized output and the true target image side by side?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}