A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy

Sakata, Yoshihito; Hirai, Ryusuke; Kobuna, Kyoka; Tanizawa, Akiyuki; Mori, Shinichiro

doi:10.1088/1361-6560/ab79c5

Cited by 19 publications

(26 citation statements)

References 23 publications

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“…To date, several studies have demonstrated the potential to eliminate the use of markers altogether through markerless tracking 35–37,50–55,57,59–64 . The implementation of markerless tracking is considered the ideal technique for the patient and the healthcare system due to its non‐invasiveness.…”

Section: Discussionmentioning

confidence: 99%

“…The system achieved an accuracy of <0.5 mm when tested on a moving chest phantom 54 . When tested on images from eight patients, the mean (±SD) tumour position error was 1.0 ± 0.3 mm in 3D space based on paired X‐ray images 55 . Furthermore, the ML algorithm was shown to be accurate when trained with DRRs generated from a simulated 4DCT 57 …”

Section: Markerless‐based Approachesmentioning

confidence: 96%

“…Mori et al 54,57 . and Sakata et al 55 . used a ML algorithm to produce a tumour likelihood map using fluoroscopic images.…”

Section: Markerless‐based Approachesmentioning

confidence: 99%

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A review of artificial intelligence applications for motion tracking in radiotherapy

2021

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During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real‐time image‐guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)‐based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker‐based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker‐based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

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Section: Discussionmentioning

confidence: 99%

Section: Markerless‐based Approachesmentioning

confidence: 96%

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A review of artificial intelligence applications for motion tracking in radiotherapy

2021

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“…The markerless tracking algorithm uses a machine learning approach. The model parameters is required for every patient using 4DCT for respective patients [7,8].…”

Section: Markerless Tracking Accuracymentioning

confidence: 99%

“…At that time, this markerless tracking technique used fluoroscopic image registration and machine learning approaches; however, it required preparation of reference data before irradiation. To solve this problem, we used a machine learning approach with treatment planning 4DCT for each patient and used it on the fluoroscopic image data acquired during irradiation to track the tumor position [7][8][9]. As we know, there is no markerless tracking algorithm with a single machine learning model file that can be applied to every patient; therefore, treatment planning 4DCT for each patient was required.…”

Section: Introductionmentioning

confidence: 99%

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning

Mori¹,

Hirai

Sakata

2020

Physica Medica

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Introduction: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (DNN) to generate 4DCT from 3DCT data. Methods: We used 2420 thoracic 4DCT datasets from 436 patients to train a DNN, designed to export 9 deformation vector fields (each field representing one-ninth of the respiratory cycle) from each CT dataset based on a 3D convolutional autoencoder with shortcut connections using deformable image registration. Then 3DCT data at exhale were transformed using the predicted deformation vector fields to obtain simulated 4DCT data. We compared markerless tracking accuracy between original and simulated 4DCT datasets for 20 patients. Our tracking algorithm used a machine learning approach with patient-specific model parameters. For the training stage, a pair of digitally reconstructed radiography images was generated using 4DCT for each patient. For the prediction stage, the tracking algorithm calculated tumor position using incoming fluoroscopic image data. Results: Diaphragmatic displacement averaged over 40 cases for the original 4DCT were slightly higher (<1.3 mm) than those for the simulated 4DCT. Tracking positional errors (95th percentile of the absolute value of displacement, "simulated 4DCT" minus "original 4DCT") averaged over the 20 cases were 0.56 mm, 0.65 mm, and 0.96 mm in the X, Y and Z directions, respectively. Conclusions: We developed a DNN to generate simulated 4DCT data that are useful for markerless tumor tracking when original 4DCT is not available. Using this DNN would accelerate markerless tumor tracking and increase treatment accuracy in thoracoabdominal treatment.

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Lung tumor segmentation in 4D CT images using motion convolutional neural networks

et al. 2021

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Purpose Manual delineation on all breathing phases of lung cancer 4D CT image datasets can be challenging, exhaustive, and prone to subjective errors because of both the large number of images in the datasets and variations in the spatial location of tumors secondary to respiratory motion. The purpose of this work is to present a new deep learning‐based framework for fast and accurate segmentation of lung tumors on 4D CT image sets. Methods The proposed DL framework leverages motion region convolutional neural network (R‐CNN). Through integration of global and local motion estimation network architectures, the network can learn both major and minor changes caused by tumor motion. Our network design first extracts tumor motion information by feeding 4D CT images with consecutive phases into an integrated backbone network architecture, locating volume‐of‐interest (VOIs) via a regional proposal network and removing irrelevant information via a regional convolutional neural network. Extracted motion information is then advanced into the subsequent global and local motion head network architecture to predict corresponding deformation vector fields (DVFs) and further adjust tumor VOIs. Binary masks of tumors are then segmented within adjusted VOIs via a mask head. A self‐attention strategy is incorporated in the mask head network to remove any noisy features that might impact segmentation performance. We performed two sets of experiments. In the first experiment, a five‐fold cross‐validation on 20 4D CT datasets, each consisting of 10 breathing phases (i.e., 200 3D image volumes in total). The network performance was also evaluated on an additional unseen 200 3D images volumes from 20 hold‐out 4D CT datasets. In the second experiment, we trained another model with 40 patients’ 4D CT datasets from experiment 1 and evaluated on additional unseen nine patients’ 4D CT datasets. The Dice similarity coefficient (DSC), center of mass distance (CMD), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and volume difference (VD) between the manual and segmented tumor contour were computed to evaluate tumor detection and segmentation accuracy. The performance of our method was quantitatively evaluated against four different methods (VoxelMorph, U‐Net, network without global and local networks, and network without attention gate strategy) across all evaluation metrics through a paired t‐test. Results The proposed fully automated DL method yielded good overall agreement with the ground truth for contoured tumor volume and segmentation accuracy. Our model yielded significantly better values of evaluation metrics (p < 0.05) than all four competing methods in both experiments. On hold‐out datasets of experiment 1 and 2, our method yielded DSC of 0.86 and 0.90 compared to 0.82 and 0.87, 0.75 and 0.83, 081 and 0.89, and 0.81 and 0.89 yielded by VoxelMorph, U‐Net, network without global and local networks, and networks without attention gate strategy. Tumor VD between ground truth and our method was the smal...

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A machine learning-based real-time tumor tracking system for fluoroscopic gating of lung radiotherapy

Cited by 19 publications

References 23 publications

A review of artificial intelligence applications for motion tracking in radiotherapy

A review of artificial intelligence applications for motion tracking in radiotherapy

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning

Lung tumor segmentation in 4D CT images using motion convolutional neural networks

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