Deep‐learning‐assisted algorithm for catheter reconstruction during MR‐only gynecological interstitial brachytherapy

Abstract

Magnetic resonance imaging (MRI) offers excellent soft-tissue contrast enabling the contouring of targets and organs at risk during gynecological interstitial brachytherapy procedure. Despite its advantage, one of the main obstacles preventing a transition to an MRI-only workflow is that implanted plastic catheters are not reliably visualized on MR images. This study aims to evaluate the feasibility of a deep-learning-based algorithm for semiautomatic reconstruction of interstitial catheters during an MR-only workflow. MR images of 20 gynecological patients were used in this study. Note that 360 catheters were reconstructed using T1-and T2-weighted images by five experienced brachytherapy planners. The mean of the five reconstructed paths were used for training (257 catheters), validation (15 catheters), and testing/evaluation (88 catheters). To automatically identify and localize the catheters, a two-dimensional (2D) U-net algorithm was used to find their approximate location in each image slice. Once localized, thresholding was applied to those regions to find the extrema, as catheters appear as bright and dark regions in T1-and T2-weighted images, respectively. The localized dwell positions of the proposed algorithm were compared to the ground truth reconstruction. Reconstruction time was also evaluated. A total of 34 009 catheter dwell positions were evaluated between the algorithm and all planners to estimate the reconstruction variability. The average variation was 0.97 ± 0.66 mm. The average reconstruction time for this approach was 11 ± 1 min, compared with 46 ± 10 min for the expert planners. This study suggests that the proposed deep learning, MR-based framework has potential to replace the conventional manual catheter reconstruction. The adoption of this approach in the brachytherapy workflow is expected to improve treatment efficiency while reducing planning time, resources, and human errors.

Review on Treatment Planning Systems for Cervix Brachytherapy (Interventional Radiotherapy): Some Desirable and Convenient Practical Aspects to Be Implemented from Radiation Oncologist and Medical Physics Perspectives

“…Shaaer et al [ 45 ] have tested an MRI line marker for the interstitial component. In a subsequent publication, with the help of those markers, the same group performed an automatic catheter segmentation using a convolutional neural network using the U-Net model, making a needle reconstruction after a post-process of the previous segmentation [ 46 ].…”

“…Nowadays, it is not available to users. Some studies have recently been published investigating the feasibility of deep learning-based algorithms for semi-automated reconstruction of interstitial catheters during MRI-based gynecological HDR [46].…”

Accuracy of an electromagnetic tracking enabled afterloader based on the automated registration with CT phantom images

“…This would allow to perform implant reconstruction, without moving the patient to a CT scan room for the sole purpose of interstitial catheter reconstruction, thus saving time and avoiding extra radiation exposure from the CT. Other advantages of an EMT‐MR GYN brachytherapy workflow would be that it could overcome image‐based limitations such as contrast and spatial resolution. Also, when compared to deep learning aproaches, 26 it does not require a retraining if changing the MRI scanner or imaging settings.…”

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

“…A U-Net model [32] was used to automatically segment Fletcher applicator with average DSC value of 0.89. A 2D U-Net algorithm [33] was tested to reconstruct the needles, with average DSC value of 0.59 and HD value of 4.2 mm, based on MR images. Two phases DL-based segmentation and object-tracking algorithms were adopted to reconstruct the interstitial needles in CT-guided prostate brachytherapy.…”