An update on computational anthropomorphic anatomical models

Akhavanallaf, Azadeh; Fayad, Hadi; Salimi, Yazdan; Aly, Antar; Naemi, Huda Al; Zaidi, Habib

doi:10.1177/20552076221111941

Cited by 7 publications

(3 citation statements)

References 88 publications

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“…Segmentation of healthy organs from Computed Tomography (CT) images is critical and beneficial in a number of applications, including the generation of anthropomorphic computational models, delimitation of organs at risk in radiation therapy (RT) treatment planning (1)(2)(3)(4), and other kinds of computer-assisted applications, such as pathologic detection (5,6), prognosis and outcome prediction (7,8), image quantification (9,10), and radiation dosimetry calculations (11)(12)(13). The manual sliceby-slice segmentation of organs can be labor-intensive and time-consuming, in addition to the high inter-and intra-observer variability reported for segmentation of healthy organs and malignant lesions (14,15).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

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Background: Automated organ segmentation from computed tomography (CT) images facilitates a number of clinical applications, including clinical diagnosis, monitoring of treatment response, quantification, radiation therapy treatment planning, and radiation dosimetry. Purpose: To develop a novel deep learning framework to generate multi-organ masks from CT images for 23 different body organs. Methods: A dataset consisting of 3106 CT images (649,398 axial 2D CT slices, 13,640 images/segment pairs) and ground-truth manual segmentation from various online available databases were collected. After cropping them to body contour, they were resized, normalized and used to train separate models for 23 organs. Data were split to train (80%) and test (20%) covering all the databases. A Res-UNET model was trained to generate segmentation masks from the input normalized CT images. The model output was converted back to the original dimensions and compared with ground-truth segmentation masks in terms of Dice and Jaccard coefficients. The information about organ positions was implemented during post-processing by providing six anchor organ segmentations as input. Our model was compared with the online available TotalSegmentator model through testing our model on their test datasets and their model on our test datasets. Results: The average Dice coefficient before and after post-processing was 84.28% and 83.26% respectively. The average Jaccard index was 76.17 and 70.60 before and after post-processing respectively. Dice coefficients over 90% were achieved for the liver, heart, bones, kidneys, spleen, femur heads, lungs, aorta, eyes, and brain segmentation masks. Post-processing improved the performance in only nine organs. Our model on the TotalSegmentator dataset was better than their models on our dataset in five organs out of 15 common organs and achieved almost similar performance for two organs. Conclusions: The availability of a fast and reliable multi-organ segmentation tool leverages implementation in clinical setting. In this study, we developed deep learning models to segment multiple body organs and compared the performance of our models with different algorithms. Our model was trained on images presenting with large variability emanating from different databases producing acceptable results even in cases with unusual anatomies and pathologies, such as splenomegaly. We recommend using these algorithms for organs providing good performance. One of the main merits of our proposed models is their lightweight nature with an average inference time of 1.67 seconds per case per organ for a total-body CT image, which facilitates their implementation on standard computers.

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Section: Introductionmentioning

confidence: 99%

Deep learning-assisted multiple organ segmentation from whole-body CT images

Salimi,

Shiri,

Mansouri

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Last but not least, the continuous advancements in artificial intelligence create more and more opportunities in nuclear medicine. Alternative algorithms for image reconstruction, artifact, attenuation and scatter correction, and more extensive dosimetry and quantitative analysis become possible [15,16]. At the same time, with the increasing availability and understanding of AI technology, we are also discovering multiple unintended biases related to race and sex that these algorithms can contain.…”

Section: Dosimetry Size and Sex-typementioning

confidence: 99%

Sex-based differences in nuclear medicine imaging and therapy

Slart

Geus‐Oei

Stevens

et al. 2023

Eur J Nucl Med Mol Imaging

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“…Modern medicine exploits advanced computational tools for assessing absorbed dose in organs of interest. To this basis, Monte Carlo (MC) simulations combined with detailed digital anthropomorphic models (Akhavanallaf et al 2022) are considered gold standard (Sarrut et al 2014). The well established MIRD dosimetry protocol considers patients' variability using interpolated S-values based on pre-defined calculations and mass correction (Bolch et al 2009).…”

Section: Introductionmentioning

confidence: 99%

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques

et al. 2023

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Goal: A methodology is introduced for the development of an internal dosimetry prediction toolkit for nuclear medical pediatric applications. The proposed study exploits Artificial Intelligence techniques using Monte Carlo simulations as ground truth for accurate prediction of absorbed doses per organ considering personalized anatomical characteristics of any new pediatric patient.Method: GATE Monte Carlo simulations were performed using a population of computational pediatric models to calculate the specific absorbed dose rates (SADRs) in several organs. A simulated dosimetry database was developed for 28 pediatric phantoms (age range 2-17 years old, both genders) and 5 different radiopharmaceuticals. Machine Learning regression models were trained on the produced simulated dataset, with Leave One Out Cross Validation for the prediction model evaluation. Hyperparameter optimization and ensemble learning techniques for a variation of input features were applied for achieving the best predictive power, leading to the development of a SADR prediction toolkit for any new pediatric patient for the studied organs and radiopharmaceuticals.Main results: SADR values for 30 organs of interest were calculated via Monte Carlo simulations for 28 pediatric phantoms for the cases of five radiopharmaceuticals. The relative percentage uncertainty in the extracted dose values per organ was lower than 2.7%. An internal dosimetry prediction toolkit which can accurately predict SADRs in 30 organs for five different radiopharmaceuticals, with mean absolute percentage error on the level of 8% was developed, with specific focus on pediatric patients, by using Machine Learning regression algorithms, Single or Multiple organ training and Artificial Intelligence ensemble techniques.Conclusion: A large dosimetry simulated database was developed and utilized for the training of Machine Learning models. The developed predictive models provide very fast results (< 2 sec) with an accuracy >90% with respect to the ground truth of Monte Carlo, considering personalized anatomical characteristics and the biodistribution of each radiopharmaceutical. The proposed method is applicable to other medical dosimetry applications in different patients’ populations.

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An update on computational anthropomorphic anatomical models

Cited by 7 publications

References 88 publications

Deep learning-assisted multiple organ segmentation from whole-body CT images

Deep learning-assisted multiple organ segmentation from whole-body CT images

Sex-based differences in nuclear medicine imaging and therapy

A framework for prediction of personalized pediatric nuclear medical dosimetry based on machine learning and Monte Carlo techniques

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