Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen

Background

Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.

Purpose

In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.

Materials and Methods

Data from 151 abdominal cancer patients treated at a 0.35 T MR-Linac (151 planning and 215 fraction MRIs) were included. Population baseline models (BMs) were trained on 107 planning MRIs for one-class segmentation of the aorta, bowel, duodenum, kidneys, liver, spinal canal, and stomach. PS models were obtained by fine-tuning the BMs using the planning MRI (${\text{PS}}_{\mathrm{BM}}$). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs (${\text{PS}}_{\mathrm{BM}}^{F4}$). Similarly, PS models without BM were trained (${\text{PS}}_{\mathrm{no}\mathrm{BM}}$ and ${\text{PS}}_{\mathrm{no}\mathrm{BM}}^{F4}$). All hyperparameters were optimized using 23 patients, and the methods were tested on the remaining 21 patients. Evaluation involved Dice similarity coefficient (DSC), average (${\text{HD}}_{\mathrm{avg}}$) and the 95^th percentile (HD₉₅) Hausdorff distance. A qualitative contour assessment by a radiation oncologist was performed for BM, ${\text{PS}}_{\mathrm{BM}}$, and ${\text{PS}}_{\mathrm{no}\mathrm{BM}}$.

Results

${\text{PS}}_{\mathrm{BM}}^{F4}$ and ${\text{PS}}_{\mathrm{BM}}$ networks had the best geometric performance. ${\text{PS}}_{\mathrm{no}\mathrm{BM}}$ and BMs showed similar DSC and HDs values, however ${\text{PS}}_{\mathrm{no}\mathrm{BM}}^{F4}$ models outperformed BMs. ${\text{PS}}_{\mathrm{BM}}$ predictions scored the best in the qualitative evaluation, followed by the BMs and ${\text{PS}}_{\mathrm{no}\mathrm{BM}}$ models.

Conclusion

Personalized auto-segmentation models outperformed the population BMs. In most cases, ${\text{PS}}_{\mathrm{BM}}$ delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment.