Xeno-learning: knowledge transfer across species in deep learning-based spectral image analysis

Differentiating between tissue types accurately and reliably, in particular with regard to pathological regions and critical anatomical structures, presents a perennial challenge in surgical interventions. Conventional RGB imaging is limited to the visible spectrum and thereby often fails to provide the necessary contrast for informed decision-making. hsi in contrast captures rich spectral signatures across dozens to hundreds of narrow wavelength bands, enabling superior tissue classification and organ boundary delineation. The widespread clinical adoption of hsi is however significantly hampered by the scarcity of large, annotated datasets that are crucial for training robust dl algorithms, which have shown great promise in the field of medical imaging. This issue is exacerbated by the inherent unpredictability of real-world intraoperative conditions as these can lead to significant domain shifts in spectral data. This thesis introduces two novel augmentation-based approaches designed to overcome the limitations of sparse data availability for robust semantic scene segmentation in hsi. The core idea is to inject relevant data samples during either model training or inference with the purpose of enhancing model generalizability. To begin with, we address the issue of pathology-related domain shifts. Domain shifts such as changes in tissue perfusion can be caused by surgical interventions and underlying health conditions. This results in malperfused organs that largely deviate from the physiological data on which models are usually trained. To reduce such occurrences, we propose a new tta strategy that employs physics-informed synthetic spectral data. We utilize physics-based simulations to produce synthetic data that compensates for perfusion-related variations as opposed to generative models that require big data sets. This tta method adjusts the distribution of unseen test images and aligns them with the training data, thus improving the robustness of models trained solely on physiological data to pathology shifts. We illustrate that this approach effectively bridges the domain gap between physiological and malperfused datasets and thus enables a more reliable segmentation performance in different clinical scenarios. Furthermore, inspired by the concept of xenotransplantation, this thesis proposes a novel ”xeno-learning” framework for cross-species knowledge transfer in medical imaging. Acquiring diverse human medical imaging data is extremely challenging due to ethical and practical constraints. Animal models (e.g., pig and rat), however, offer more readily available and diverse datasets, including a wider array of tissue conditions. Our main observation is that, despite the fact that the absolute spectral characteristics of tissues may differ across species, the relative spectral changes caused by physiological changes (e.g. changes in organ perfusion or the administration of contrast agents like icg), tend to remain consistent. This forms the basis of our xeno-learning framework. We develop a novel ”physiology-based data augmentation” technique that transfers knowledge about domain shifts learned from animal data to augment human data during training. This enables models to generalize effectively to unseen pathological human test datasets. We demonstrate that xeno-learning not only enables successful knowledge transfer but also outperforms traditional adversarial domain adaptation methods, highlighting its potential for improving model generalizability between species To summarize, this thesis establishes the feasibility of automatic semantic scene segmentation on human physiological HSI data and offers innovative solutions that address critical data scarcity and domain shift challenges. The proposed tta strategy and xeno-learning framework offer robust pathways for enhancing the generalizability and clinical applicability of DL-based spectral image analysis. This allows for advancements in robotically-assisted surgery, organ-aware perfusion assessment, and ar/vr-based surgical guidance.