Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL.

Autor: Choi BS; Department of Radiation Oncology, Mayo Clinic, Florida, USA.; Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.; Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul, South Korea., Beltran CJ; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Olberg S; Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA., Liang X; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Lu B; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Tan J; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Parisi A; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Denbeigh J; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Yaddanapudi S; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Kim JS; Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, South Korea.; Medical Physics and Biomedical Engineering Lab (MPBEL), Yonsei University College of Medicine, Seoul, South Korea.; OncoSoft. Inc, Seoul, South Korea., Furutani KM; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Park JC; Department of Radiation Oncology, Mayo Clinic, Florida, USA., Song B; Department of Radiation Oncology, University of California San Diego, San Diego, California, USA.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2024 Nov; Vol. 51 (11), pp. 8568-8583. Date of Electronic Publication: 2024 Aug 21.
DOI: 10.1002/mp.17361
Abstrakt: Background: Adaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real-time or online ART workflow. In response to this challenge, approaches to auto-segmentation involving deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model.
Purpose: To address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto-segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)-IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation.
Methods: The PHL-IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL-IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients.
Results: Implementing the PHL-IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.81 ± $ \pm $ 0.05 with the general model, 0.83 ± 0.04 $ \pm 0.04$ for the continual model, 0.83 ± 0.04 $ \pm 0.04$ for the conventional IDOL model to an average of 0.87 ± 0.03 $ \pm 0.03$ with the PHL-IDOL model. Similarly, the Hausdorff distance decreased from 3.06 ± 0.99 $ \pm 0.99$ with the general model, 2.84 ± 0.69 $ \pm 0.69$ for the continual model, 2.79 ± 0.79 $ \pm 0.79$ for the conventional IDOL model and 2.36 ± 0.52 $ \pm 0.52$ for the PHL-IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL-IDOL model.
Conclusion: The PHL-IDOL framework applied to the auto-segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient-specific prior information in a task central to online ART workflows.
(© 2024 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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