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

Abstract

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.

Keywords: MR‐Linac; auto‐segmentation; patient‐specific transfer learning.

FIGURE 1

Workflow of the investigated training strategies. The boxes represent the investigated models, while the arrows indicate the process of training or fine‐tuning. The organ‐specific one‐class population BMs were trained on a cohort of 107 patients. Subsequently, BMs were fine‐tuned by PS training either with the planning (Plan MRI) or the planning and the first $N=4$ F images yielding ${\text{PS}}_{\mathrm{BM}}$ and ${\text{PS}}_{\mathrm{BM}}^{F4}$ models, respectively. Repeating the process without the BMs for initializing the model weights and biases resulted in ${\text{PS}}_{\mathrm{no}\mathrm{BM}}$ and ${\text{PS}}_{\mathrm{no}\mathrm{BM}}^{F4}$ models, respectively. BMs, baseline models; F, fraction; MRI, magnetic resonance imaging; PS, patient‐specific.

FIGURE 2

Exemplary validation curves for ${\text{PS}}_{\mathrm{BM}}$ training for the aorta, bowel, and right kidney. The upper panel displays individual DSC curves for each patient across the training epochs. In the lower panel, cumulative curves depict average DSC scores across all validation patients. DSC for epoch 0 corresponds to the BM performance. BM, baseline model; DSC, dice similarity coefficient.

FIGURE 3

Axial view of an exemplary test patient showing predictions of (dashed lines) deep learning models versus (half‐transparent background) the clinical ground truth. Predictions of the following models are shown: the BMs, PS models generated by fine‐tuning the BMs with the planning (${\text{PS}}_{\mathrm{BM}}$) or the planning and first four fraction MRIs (${\text{PS}}_{\mathrm{BM}}^{F4}$), models trained from scratch only with the planning (${\text{PS}}_{\mathrm{no}\mathrm{BM}}$) or with the planning and first four fraction MRIs (${\text{PS}}_{\mathrm{no}\mathrm{BM}}^{F4}$). BMs, baseline models; MRI, magnetic resonance imaging; PS, patient‐specific.

FIGURE 4

Box plots presenting DSC, 95 percentile (${\text{HD}}_{95}$), and average (${\text{HD}}_{\mathrm{avg}}$) Hausdorff distance for the 5^th fractions of the 21 test patients. For all organs‐at‐risk the performance of the following models are compared: the BMs, PS models generated by fine‐tuning the BMs with one (${\text{PS}}_{\mathrm{BM}}$) and five MRIs (${\text{PS}}_{\mathrm{BM}}^{F4}$), as well as PS models trained from scratch with one (${\text{PS}}_{\mathrm{no}\mathrm{BM}}$) and with 5 MRIs (${\text{PS}}_{\mathrm{BM}}^{F4}$) of a given patient. BMs, baseline models; HD, Hausdorff distance; MRI, magnetic resonance imaging; PS, patient‐specific.

FIGURE 5

Bar plots displaying radiation oncologist's grading of predictions generated by the BMs, PS models fine‐tuning the BM with the planning MRI (${\text{PS}}_{\mathrm{BM}}$) and models trained from scratch with the planning MRI (${\text{PS}}_{\mathrm{no}\mathrm{BM}}$). The grades range from ”ideal” to ”unusable”. BMs, baseline models; MRI, magnetic resonance imaging; PS, patient‐specific.