Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

Lappas, Georgios; Staut, Nick; Lieuwes, Natasja G.; Biemans, Rianne; Wolfs, Claire; Hoof, Stefan J van; Dubois, Ludwig; Verhaegen, Frank

doi:10.1016/j.phro.2022.01.002

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…Overall, the proposed DL model and accompanying preprocessing method provide high quality contours for normal tissues in preclinical studies, that are generated fully automatically after training on manual expert segmentations. For most of the organs evaluated in this work, sub-millimeter performance was achieved, which was within the range of the previously studied IOV (Lappas et al 2022). Relative to the organ's sizes and the voxel resolution, the sub-millimeter differences between manual and DL generated contours can be considered small.…”

Section: Discussionsupporting

confidence: 70%

“…Moreover, figure 4 also indicates the median and interquartile range inter-observer variability (IOV) for manual contouring that was previously calculated by Lappas et al (2022). Generally, for the brain (whole as well as the hemispheres separately) and spinal cord, the median DL model performance is within the range of the IOV.…”

Section: Resultsmentioning

confidence: 87%

“…The lungs, spinal cord, thorax bone and brain hemispheres were contoured semi-automatically, while the other organs were delineated fully manually. Further details of the delineation techniques used are provided by Lappas et al (2022).…”

Section: Manual Organ Segmentationmentioning

confidence: 99%

“…As a subset of the head and thorax datasets (20 cases of each) was used for an inter-observer variability study (Lappas et al 2022), manual contours from both animal technicians were available for the cases in this subset. For the cases not in this subset, only one set of manual contours was available.…”

Section: Manual Organ Segmentationmentioning

confidence: 99%

“…It involves precise delineation of relevant tissue volumes using 2D polygons adjacent to the borders of these tissues, until the whole tissue is covered, introducing variances dependent on several factors. Furthermore, inter-observer variability can introduce bias into the treatment plan because of anatomical complexities and lack of guidelines for murine organ delineations (Schoppe et al 2020, Lappas et al 2022.…”

Section: Introductionmentioning

confidence: 99%

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Automatic contouring of normal tissues with deep learning for preclinical radiation studies

et al. 2022

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Objective: Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (μCBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies. Approach: A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of the μCBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (∆CoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed – 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout (MCD) uncertainty was performed for delineation of the heart. Main results: The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD95p < 1 mm and ∆CoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%). Significance: A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low.

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Section: Discussionsupporting

confidence: 70%

Section: Resultsmentioning

confidence: 87%

Section: Manual Organ Segmentationmentioning

confidence: 99%

Section: Manual Organ Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic contouring of normal tissues with deep learning for preclinical radiation studies

et al. 2022

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Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study

et al. 2023

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The aim of this study is to investigate the use of an exponential-plateau model to determine the required training dataset size that yields the maximum medical image segmentation performance. CT and MR images of patients with renal tumors acquired between 1997 and 2017 were retrospectively collected from our nephrectomy registry. Modality-based datasets of 50, 100, 150, 200, 250, and 300 images were assembled to train models with an 80–20 training-validation split evaluated against 50 randomly held out test set images. A third experiment using the KiTS21 dataset was also used to explore the effects of different model architectures. Exponential-plateau models were used to establish the relationship of dataset size to model generalizability performance. For segmenting non-neoplastic kidney regions on CT and MR imaging, our model yielded test Dice score plateaus of $$0.93\pm 0.02$$ 0.93 ± 0.02 and $$0.92\pm 0.04$$ 0.92 ± 0.04 with the number of training-validation images needed to reach the plateaus of 54 and 122, respectively. For segmenting CT and MR tumor regions, we modeled a test Dice score plateau of $$0.85\pm 0.20$$ 0.85 ± 0.20 and $$0.76\pm 0.27$$ 0.76 ± 0.27 , with 125 and 389 training-validation images needed to reach the plateaus. For the KiTS21 dataset, the best Dice score plateaus for nn-UNet 2D and 3D architectures were $$0.67\pm 0.29$$ 0.67 ± 0.29 and $$0.84\pm 0.18$$ 0.84 ± 0.18 with number to reach performance plateau of 177 and 440. Our research validates that differing imaging modalities, target structures, and model architectures all affect the amount of training images required to reach a performance plateau. The modeling approach we developed will help future researchers determine for their experiments when additional training-validation images will likely not further improve model performance.

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Segment anything model for medical image segmentation: Current applications and future directions

Zhang,

Shen,

Jiao

2024

Computers in Biology and Medicine

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Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

Cited by 11 publications

References 25 publications

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study

Segment anything model for medical image segmentation: Current applications and future directions

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