Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs

Amjad, A.; Xu, Jiaofeng; Thill, D.; Zhang, Ying; Ding, Jie; Paulson, E.S.; Hall, William A.; Erickson, Beth; Li, X. Allen

doi:10.3389/fonc.2023.1209558

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Amjad et al. ( 42 ) used multi-sequence MR images acquired from 3.0 T MR device for training to segment abdominal organs, achieving better segmentation results for the kidneys, the duodenum and the stomach. These improvements might be attributed to their utilization of a diagnostic MR device avoiding MR-Linac possible artefacts ( 43 ), offering a higher magnetic field strength with a better image contrast and a training based on several MR contrasts.…”

Section: Discussionmentioning

confidence: 99%

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images

Zhou,

Lalande,

Chevalier

et al. 2024

Front. Oncol.

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IntroductionLinear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images.MethodsOne hundred and twenty one sets of abdominal MR images and their corresponding ground truth segmentations were collected and used for this work. The OARs of interest included the liver, the kidneys, the spinal cord, the stomach and the duodenum. Several UNet based models have been trained in 2D (the Classical UNet, the ResAttention UNet, the EfficientNet UNet, and the nnUNet). The best model was then trained with a 3D strategy in order to investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) and analysis of the calculated volumes (thanks to Bland-Altman plot) were performed to evaluate the results.ResultsThe nnUNet trained in 3D mode achieved the best performance, with DSC scores for the liver, the kidneys, the spinal cord, the stomach, and the duodenum of 0.96 ± 0.01, 0.91 ± 0.02, 0.91 ± 0.01, 0.83 ± 0.10, and 0.69 ± 0.15, respectively. The matching IoU scores were 0.92 ± 0.01, 0.84 ± 0.04, 0.84 ± 0.02, 0.54 ± 0.16 and 0.72 ± 0.13. The corresponding HD scores were 13.0 ± 6.0 mm, 16.0 ± 6.6 mm, 3.3 ± 0.7 mm, 35.0 ± 33.0 mm, and 42.0 ± 24.0 mm. The analysis of the calculated volumes followed the same behavior.DiscussionAlthough the segmentation results for the duodenum were not optimal, these findings imply a potential clinical application of the 3D nnUNet model for the segmentation of abdominal OARs for images from 0.35 T MR-Linac.

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Section: Discussionmentioning

confidence: 99%

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images

Zhou,

Lalande,

Chevalier

et al. 2024

Front. Oncol.

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Editorial for “A Fully‐Automatic Method to Segment Choroid Plexuses in Multiple Sclerosis Using Conventional MRI Sequences”

Duan,

2023

Magnetic Resonance Imaging

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Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours

Zarenia,

Zhang,

Sarosiek

et al. 2024

Phys. Med. Biol.

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Objective. Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach. The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results. The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/136) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance. We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.

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Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs

Cited by 3 publications

References 28 publications

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images

Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images

Editorial for “A Fully‐Automatic Method to Segment Choroid Plexuses in Multiple Sclerosis Using Conventional MRI Sequences”

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours

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