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arXiv · 2510.09326

MIP-Based Tumor Segmentation: A Radiologist-Inspired Approach

Romario Zarik·Nahum Kiryati·Michael Green·Liran Domachevsky·Arnaldo Mayer
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Abstract

PET/CT imaging is the gold standard for tumor detection, offering high accuracy in identifying local and metastatic lesions. Radiologists often begin assessment with rotational Multi-Angle Maximum Intensity Projections (MIPs) from PET, confirming findings with volumetric slices. This workflow is time-consuming, especially in metastatic cases. Despite their clinical utility, MIPs are underutilized in automated tumor segmentation, where 3D volumetric data remains the norm. We propose an alternative approach that trains segmentation models directly on MIPs, bypassing the need to segment 3D volumes and then project. This better aligns the model with its target domain and yields substantial gains in computational efficiency and training time. We also introduce a novel occlusion correction method that restores MIP annotations occluded by high-intensity structures, improving segmentation. Using the autoPET 2022 Grand Challenge dataset, we evaluate our method against standard 3D pipelines in terms of performance and training/computation efficiency for segmentation and classification, and analyze how MIP count affects segmentation. Our MIP-based approach achieves segmentation performance on par with 3D (<=1% Dice difference, 26.7% better Hausdorff Distance), while reducing training time (convergence time) by 55.8-75.8%, energy per epoch by 71.7-76%, and TFLOPs by two orders of magnitude, highlighting its scalability for clinical use. For classification, using 16 MIPs only as input, we surpass 3D performance while reducing training time by over 10x and energy consumption per epoch by 93.35%. Our analysis of the impact of MIP count on segmentation identified 48 views as optimal, offering the best trade-off between performance and efficiency.

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Romario Zarik, Nahum Kiryati, Michael Green, Liran Domachevsky, Arnaldo Mayer. 2025-10-10. MIP-Based Tumor Segmentation: A Radiologist-Inspired Approach. https://arxiv.org/abs/2510.09326

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