Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
Organ segmentation in Positron Emission Tomography (PET) plays a vital role
in cancer quantification. Low-dose PET (LDPET) provides a safer alternative by
reducing radiation exposure. However, the inherent noise and blurred boundaries
make organ segmentation more challenging. Additionally, existing PET organ
segmentation methods rely on co-registered Computed Tomography (CT)
annotations, overlooking the problem of modality mismatch. In this study, we
propose LDOS, a novel CT-free ultra-LDPET organ segmentation pipeline. Inspired
by Masked Autoencoders (MAE), we reinterpret LDPET as a naturally masked
version of Full-Dose PET (FDPET). LDOS adopts a simple yet effective
architecture: a shared encoder extracts generalized features, while
task-specific decoders independently refine outputs for denoising and
segmentation. By integrating CT-derived organ annotations into the denoising
process, LDOS improves anatomical boundary recognition and alleviates the
PET/CT misalignments. Experiments demonstrate that LDOS achieves
state-of-the-art performance with mean Dice scores of 73.11% (18F-FDG) and
73.97% (68Ga-FAPI) across 18 organs in 5% dose PET. Our code is publicly
available.
