Automated Lesion Segmentation in Whole-Body PET/CT and Longitudinal (autoPET/CT IV)
Creators: name: Küstner, Thomas
name: Gatidis, Sergios
name: Megne, Ornela
name: Ingrisch, Michael
name: Fabritius, Matthias
name: Dexl, Jakob
name: Jeblick, Katharina
name: Mittermeier, Andreas
name: Schachtner, Balthasar
name: Stüber, Theresa
name: Cyran, Clemens
name: Marinov, Zdravko
name: Peisen, Felix
name: Wagner, Andreas
name: Othman, Ahmed
name: Sanner, Antoine
name: Loßau, Tanja
name: Moltz, Jan Hendrik
name: Kohlbrandt, Temke
name: Hering, Alessa
Publisher: name: Zenodo
Year: 2025
Subjects: PET/CT
CT
tumor
segmentation
interactive segmentation
MICCAI 2025 challenge

description: Positron Emission Tomography / Computed Tomography (PET/CT) and CT are an integral part of the diagnostic workup for various malignant solid tumor entities. Currently, response assessment for cancer treatment is performed by radiologists (i.e. human observers) on consecutive PET/CT or CT scans through the detection of changes in tumour size and distribution using standardised criteria. Despite the highly time-consuming nature of this manual task, only unidimensional (diameter) evaluations of a subset of tumour lesions are used to assess tumour dynamics. Additional quantitative evaluation of PET information would potentially allow for more precise and individualized diagnostic decisions. Besides the risk of inter-observer variability, the manual approach only extracts a diminutive proportion of the morphologic tumour data derived from images, thereby neglecting valuable, significant, and prognostic information.

Automation of tumour detection and segmentation as well as longitudinal evaluation may enable faster and more comprehensive information and data extraction. However, automated solutions for this task are lacking. AI-based approaches, using deep-learning models, may be an appropriate way to address lesion detection andsegmentation in whole-body hybrid imaging (PET/CT and CT) to compensate workload and time pressure during radiological readings. So far, most AI solutions analyse isolated scans at single time points, and/or from a single imaging modality and thus information from preliminary or additional examinations is excluded. Moreover, methods are often prone to specialize to specific imaging conditions (imaging scanner, lesion phenotype, PET tracer, and so on) rendering it a challenge to generalize for different imaging scenarios. In addition, the necessity or potential benefit of integrating human experts in the training and/or inference loop is not yet explored in this setting.

In the first run of the autoPET challenge (autoPET I at MICCAI 2022) we provided training and test data from University Hospital Tübingen (UKT) and LMU Hospital (LMU) as well as a baseline model for automated lesion segmentation on whole-body Fluorodeoxyglucose (FDG) PET/ CT data. The challenge results demonstrated the feasibility of accurate lesion segmentation. In the second edition of the autoPET challenge (autoPET II at MICCAI 2023) we shifted our focus towards evaluating algorithmic robustness across different environments. This involved introducing a new test set comprising samples from various sites, utilizing different tracers and presenting diverse pathologies. We also relaxed constraints on using external and additional data. The challenge results revealed a substantial decline in performance, highlighting the critical need for the  development of robust algorithms. Based on these insights, the scope of the third edition of the autoPET challenge (autoPET III at MICCAI 2024) was to achieve multitracer and multicenter generalization of automated lesion segmentation. To this end, we provided participants access to a second, large Prostate-Specific Membrane Antigen (PSMA) whole-body PET/CT training dataset. The challenge results demonstrated good efforts towards PET tracer (FDG and PSMA) generalization with an increased usage of appropriate data processing pipelines (data-centric approach) and pre-trained or foundational models. A key challenge that remained was accurate lesion detection under changing scenarios, i.e. keeping low false positives and false negatives. The specific difficulty lies in the facts that not only tumor lesions but also healthy organs (e.g. the brain) can have significant uptake. Hence, lesions cannot be well delineated against their surroundings, and in addition individual tumor instances cannot be separated from a cluster of tumor lesions.

We propose to focus the fourth challenge autoPET/CT IV on lesion segmentation within a clinical-realistic setting  of single-staging PET/CT and longitudinal CT monitoring. For this purpose we will investigate an interactive  human-in-the-loop scenario to identify the potential benefits, requirements and necessities for hybrid AI methods. Algorithms are provided with varying levels of annotations with the aim to investigate the model conditioning on label information for two imaging scenarios: single-staging PET/CT and longitudinal CT. This concept aligns closely with real-world clinical workflows, enabling users to specify particular lesion instances for precise segmentation and longitudinal tracking over time. To this end, we provide a third, large longitudinal CT training dataset of melanoma patients under therapy. We allow the submission of data-centric solutions (using the provided baselines), integration and interaction with foundation models, using/extending pre-trained algorithms, or the developments of novel algorithms.