# Cross-Source Supervision for Bone Infection Segmentation in Dual-Modality PET-CT

arXiv: https://arxiv.org/abs/2605.16373 · HTML: https://mlsanity.com/paper/2605.16373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 7 of 7)

- [On the Effect of Inter-observer Variability for a Reliable Estimation of Uncertainty of Medical Image Segmentation](https://mlsanity.com/paper/1806.02562/) (2018)
- [CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising](https://mlsanity.com/paper/2207.07798/) (2022)
- [RZCR: Zero-shot Character Recognition via Radical-based Reasoning](https://mlsanity.com/paper/2207.05842/) (2022)
- [SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation](https://mlsanity.com/paper/2409.13321/) (2024)
- [Competitive Distillation: A Simple Learning Strategy for Improving Visual Classification](https://mlsanity.com/paper/2506.23285/) (2025)
- [KAE: A Property-based Method for Knowledge Graph Alignment and Extension](https://mlsanity.com/paper/2407.05320/) (2024)
- [Minuscule Cell Detection in AS-OCT Images with Progressive Field-of-View Focusing](https://mlsanity.com/paper/2503.12249/) (2025)

## Abstract (arXiv)

Early and accurate diagnosis and lesion localization of bone infections are crucial for clinical treatment. PET-CT integrates anatomical information from CT with metabolic information from PET, making it an important imaging modality for diagnosing bone infections. However, accurate lesion segmentation remains challenging due to indistinct lesion boundaries and inconsistencies in annotations generated by different experts or automated systems. In this work, we investigate multimodal segmentation of bone infections under annotation discrepancy. We develop a bimodal end-to-end segmentation framework that integrates PET metabolic signals and CT bone-window anatomy through an early-fusion multimodal representation.To mitigate performance inflation caused by inter-slice correlation in small datasets, this study discards traditional two-dimensional evaluation methods and implements a rigorous patient-level 3D volumetric evaluation and cross-validation. Furthermore, instead of forcing a singular consensus, we propose a decoupled dual-source learning framework where parallel models are trained on independent expert annotations driven by high-sensitivity and high-specificity clinical intents. Experimental results objectively report performance variations at the patient level (Mean + SD and Mean - SD), demonstrating the effectiveness of multimodal PET-CT fusion. The cross-evaluation matrix quantitatively reveals how models successfully internalize distinct expert diagnostic philosophies, providing a robust, diversity-preserving paradigm for clinical AI deployment in bone infection segmentation.