Medical Imaging and Analysis

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• Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT

  Matan Atad, Alexander W. Marka, Lisa Steinhelfer, Anna Curto-Vilalta, Yannik Leonhardt, Sarah C. Foreman, Anna-Sophia Walburga Dietrich, Robert Graf, Alexandra S. Gersing, Bjoern Menze, Daniel Rueckert, Jan S. Kirschke, Hendrik M\"oller · 9 décembre 2025

  Accurate segmentation of vertebral metastasis in CT is clinically important yet difficult to scale, as voxel-level annotations are scarce and both lytic and blastic lesions often resemble benign degenerative changes. We introduce a weakly supervised method trained solely on vertebra-level healthy/ma…

• 3D Path Planning for Robot-assisted Vertebroplasty from Arbitrary Bi-plane X-ray via Differentiable Rendering

  Blanca Inigo, Benjamin D. Killeen, Rebecca Choi, Michelle Song, Ali Uneri, Majid Khan, Christopher Bailey, Axel Krieger, Mathias Unberath · 8 décembre 2025

  Robotic systems are transforming image-guided interventions by enhancing accuracy and minimizing radiation exposure. A significant challenge in robotic assistance lies in surgical path planning, which often relies on the registration of intraoperative 2D images with preoperative 3D CT scans. This re…

• Connecting Neural Models Latent Geometries with Relative Geodesic Representations

  Hanlin Yu, Berfin Inal, Georgios Arvanitidis, Soren Hauberg, Francesco Locatello, Marco Fumero · 2 décembre 2025

  Neural models learn representations of high-dimensional data on low-dimensional manifolds. Multiple factors, including stochasticities in the training process, model architectures, and additional inductive biases, may induce different representations, even when learning the same task on the same dat…

• Developing Fairness-Aware Task Decomposition to Improve Equity in Post-Spinal Fusion Complication Prediction

  Yining Yuan, J. Ben Tamo, Wenqi Shi, Yishan Zhong, Micky C. Nnamdi, B. Randall Brenn, Steven W. Hwang, May D. Wang · 2 décembre 2025

  Fairness in clinical prediction models remains a persistent challenge, particularly in high-stakes applications such as spinal fusion surgery for scoliosis, where patient outcomes exhibit substantial heterogeneity. Many existing fairness approaches rely on coarse demographic adjustments or post-hoc …

• Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment

  Zixue Zeng, Xiaoyan Zhao, Matthew Cartier, Tong Yu, Jing Wang, Xin Meng, Zhiyu Sheng, Maryam Satarpour, John M Cormack, Allison Bean, Ryan Nussbaum, Maya Maurer, Emily Landis-Walkenhorst, Dinesh Kumbhare, Kang Kim, Ajay Wasan, Jiantao Pu · 26 novembre 2025

  We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhanc…

• Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours

  Roman Flepp, Leon Nissen, Bastian Sigrist, Arend Nieuwland, Nicola Cavalcanti, Philipp F\"urnstahl, Thomas Dreher, Lilian Calvet · 25 novembre 2025

  Purpose: Accurate intraoperative X-ray/CT registration is essential for surgical navigation in orthopedic procedures. However, existing methods struggle with consistently achieving sub-millimeter accuracy, robustness under broad initial pose estimates or need manual key-point annotations. This work …

• Prediction of Clinical Complication Onset using Neural Point Processes

  Sachini Weerasekara, Sagar Kamarthi, Jacqueline Isaacs · 25 novembre 2025

  Predicting medical events in advance within critical care settings is paramount for patient outcomes and resource management. Utilizing predictive models, healthcare providers can anticipate issues such as cardiac arrest, sepsis, or respiratory failure before they manifest. Recently, there has been …

• US-X Complete: A Multi-Modal Approach to Anatomical 3D Shape Recovery

  Miruna-Alexandra Gafencu, Yordanka Velikova, Nassir Navab, Mohammad Farid Azampour · 20 novembre 2025

  Ultrasound offers a radiation-free, cost-effective solution for real-time visualization of spinal landmarks, paraspinal soft tissues and neurovascular structures, making it valuable for intraoperative guidance during spinal procedures. However, ultrasound suffers from inherent limitations in visuali…

• TomoGraphView: 3D Medical Image Classification with Omnidirectional Slice Representations and Graph Neural Networks

  Johannes Kiechle, Stefan M. Fischer, Daniel M. Lang, Cosmin I. Bercea, Matthew J. Nyflot, Lina Felsner, Julia A. Schnabel, Jan C. Peeken · 14 novembre 2025

  The growing number of medical tomography examinations has necessitated the development of automated methods capable of extracting comprehensive imaging features to facilitate downstream tasks such as tumor characterization, while assisting physicians in managing their growing workload. However, 3D m…

• A Survey on Human-Centered Evaluation of Explainable AI Methods in Clinical Decision Support Systems

  Alessandro Gambetti, Qiwei Han, Hong Shen, Claudia Soares · 12 novembre 2025

  Explainable Artificial Intelligence (XAI) is essential for the transparency and clinical adoption of Clinical Decision Support Systems (CDSS). However, the real-world effectiveness of existing XAI methods remains limited and is inconsistently evaluated. This study conducts a systematic PRISMA-guided…

• Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures

  Florence Klitzner, Blanca Inigo, Benjamin D. Killeen, Lalithkumar Seenivasan, Michelle Song, Axel Krieger, Mathias Unberath · 7 novembre 2025

  Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided procedures, such as spine instrumentation. This is because interpretation of multi-view X-rays is complex. We examine oppor…

• Expert Validation of Synthetic Cervical Spine Radiographs Generated with a Denoising Diffusion Probabilistic Model

  Austin A. Barr, Brij S. Karmur, Anthony J. Winder, Eddie Guo, John T. Lysack, James N. Scott, William F. Morrish, Muneer Eesa, Morgan Willson, David W. Cadotte, Michael M. H. Yang, Ian Y. M. Chan, Sanju Lama, Garnette R. Sutherland · 28 octobre 2025

  Machine learning in neurosurgery is limited by challenges in assembling large, high-quality imaging datasets. Synthetic data offers a scalable, privacy-preserving solution. We evaluated the feasibility of generating realistic lateral cervical spine radiographs using a denoising diffusion probabilist…

• SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus

  Ming Zhao, Wenhui Dong, Yang Zhang, Xiang Zheng, Zhonghao Zhang, Zian Zhou, Yunzhi Guan, Liukun Xu, Wei Peng, Zhaoyang Gong, Zhicheng Zhang, Dachuan Li, Xiaosheng Ma, Yuli Ma, Jianing Ni, Changjiang Jiang, Lixia Tian, Qixin Chen, Kaishun Xia, Pingping Liu, Tongshun Zhang, Zhiqiang Liu, Zhongyan Bi, Chenyang Si, Tiansheng Sun, Caifeng Shan · 28 octobre 2025

  Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific ver…

• Dynamic-Aware Spatio-temporal Representation Learning for Dynamic MRI Reconstruction

  Dayoung Baik, Jaejun Yoo · 28 octobre 2025

  Dynamic MRI reconstruction, one of inverse problems, has seen a surge by the use of deep learning techniques. Especially, the practical difficulty of obtaining ground truth data has led to the emergence of unsupervised learning approaches. A recent promising method among them is implicit neural repr…