Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL

doi:10.1002/mp.17361

Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL

2024-08-21

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DOI：10.1002/mp.17361

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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 +/-+/- 0.05 with the general model, 0.83 +/- 0.04 +/- 0.04 for the continual model, 0.83 +/- 0.04 +/- 0.04 for the conventional IDOL model to an average of 0.87 +/- 0.03 +/- 0.03 with the PHL-IDOL model. Similarly, the Hausdorff distance decreased from 3.06 +/- 0.99 +/- 0.99 with the general model, 2.84 +/- 0.69 +/- 0.69 for the continual model, 2.79 +/- 0.79 +/- 0.79 for the conventional IDOL model and 2.36 +/- 0.52 +/- 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.

Keywords:

ART

auto segmentation

deep learning

head & neck

overfitting

Enhanced IDOL segmentation framework using personalized hyperspace learning IDOL

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