Medical physics · 2024
Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.
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Kawula Maria, Marschner Sebastian, Wei Chengtao, Ribeiro Marvin F, Corradini Stefanie, Belka Claus, Landry Guillaume, Kurz Christopher
Аннотация
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 ( PS BM $\text{PS}_{\mathrm{BM}}$ ). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs ( PS BM F4 $\text{PS}_{\mathrm{BM}}^{\operatorname{F4}}$ ). Similarly, PS models without BM were trained ( PS no BM $\text{PS}_{\mathrm{no BM}}$ and PS no BM F4 $\text{PS}_{\mathrm{no BM}}^{\operatorname{F4}}$ ). All hyperparameters were optimized using 23 patients, and the methods were tested on the remaining 21 patients. Evaluation involved Dice similarity coefficient (DSC), average ( HD avg $\text{HD}_{\rm avg}$ ) and the 95th percentile (HD95) Hausdorff distance. A qualitative contour assessment by a radiation oncologist was performed for BM, PS BM $\text{PS}_{\mathrm{BM}}$ , and PS no BM $\text{PS}_{\mathrm{no BM}}$ .
RESULTS: PS BM F4 $\text{PS}_{\mathrm{BM}}^{\operatorname{F4}}$ and PS BM $\text{PS}_{\mathrm{BM}}$ networks had the best geometric performance. PS no BM $\text{PS}_{\mathrm{no BM}}$ and BMs showed similar DSC and HDs values, however PS no BM F4 $\text{PS}_{\mathrm{no BM}}^{\operatorname{F4}}$ models outperformed BMs. PS BM $\text{PS}_{\mathrm{BM}}$ predictions scored the best in the qualitative evaluation, followed by the BMs and PS no BM $\text{PS}_{\mathrm{no BM}}$ models.
CONCLUSION: Personalized auto-segmentation models outperformed the population BMs. In most cases, PS BM $\text{PS}_{\mathrm{BM}}$ delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment.
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 ( PS BM $\text{PS}_{\mathrm{BM}}$ ). Maximal improvement by continuously updating the PS models was investigated by adding the first four out of five fraction MRIs ( PS BM F4 $\text{PS}_{\mathrm{BM}}^{\operatorname{F4}}$ ). Similarly, PS models without BM were trained ( PS no BM $\text{PS}_{\mathrm{no BM}}$ and PS no BM F4 $\text{PS}_{\mathrm{no BM}}^{\operatorname{F4}}$ ). All hyperparameters were optimized using 23 patients, and the methods were tested on the remaining 21 patients. Evaluation involved Dice similarity coefficient (DSC), average ( HD avg $\text{HD}_{\rm avg}$ ) and the 95th percentile (HD95) Hausdorff distance. A qualitative contour assessment by a radiation oncologist was performed for BM, PS BM $\text{PS}_{\mathrm{BM}}$ , and PS no BM $\text{PS}_{\mathrm{no BM}}$ .
RESULTS: PS BM F4 $\text{PS}_{\mathrm{BM}}^{\operatorname{F4}}$ and PS BM $\text{PS}_{\mathrm{BM}}$ networks had the best geometric performance. PS no BM $\text{PS}_{\mathrm{no BM}}$ and BMs showed similar DSC and HDs values, however PS no BM F4 $\text{PS}_{\mathrm{no BM}}^{\operatorname{F4}}$ models outperformed BMs. PS BM $\text{PS}_{\mathrm{BM}}$ predictions scored the best in the qualitative evaluation, followed by the BMs and PS no BM $\text{PS}_{\mathrm{no BM}}$ models.
CONCLUSION: Personalized auto-segmentation models outperformed the population BMs. In most cases, PS BM $\text{PS}_{\mathrm{BM}}$ delineations were judged to be directly usable for treatment adaptation without further corrections, suggesting a potential time saving during fractionated treatment.