Volumetric quantitative measurement of hip effusions by manual versus automated artificial intelligence techniques: An OMERACT preliminary validation study

Jaremko, Jacob L.; Felfeliyan, Banafshe; Hareendranathan, Abhilash Rakkunedeth; Thejeel, Bashiar; Quinn-Laurin, Vanessa; Østergaard, Mikkel; Conaghan, Philip G.; Lambert, Robert G.; Ronsky, Janet L.; Maksymowych, Walter P.

doi:10.1016/j.semarthrit.2021.03.009

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…This success may be attributed to the utilization of Mask R-CNN, which is a versatile and compact framework for object instance segmentation. This model not only detects targets within the image but also provides pixel-level segmentation results for each individual target [24]. The PR curves clearly indicate that the model's performance starts to decrease signi cantly when the IoU is set above 0.9.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Ameloblastoma on CT Images using Deep Learning with Limited Data

Xu,

Qiu,

et al. 2023

Preprint

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Background Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask RCNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion In this study, we successfully developed a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Ameloblastoma on CT Images using Deep Learning with Limited Data

Xu,

Qiu,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This success may be attributed to the utilization of Mask R-CNN, which is a versatile and compact framework for object instance segmentation. This model not only detects targets within the image but also provides pixel-level segmentation results for each individual target [ 24 ]. The PR curves clearly indicate that the model's performance starts to decrease significantly when the IoU is set above 0.9.…”

Section: Discussionmentioning

confidence: 99%

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data

Xu,

Qiu,

et al. 2024

BMC Oral Health

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Background Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

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Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative)

Felfeliyan

Hareendranathan

Kuntze

et al. 2022

Computerized Medical Imaging and Graphics

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Volumetric quantitative measurement of hip effusions by manual versus automated artificial intelligence techniques: An OMERACT preliminary validation study

Cited by 8 publications

References 8 publications

Automatic Segmentation of Ameloblastoma on CT Images using Deep Learning with Limited Data

Automatic Segmentation of Ameloblastoma on CT Images using Deep Learning with Limited Data

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data

Improved-Mask R-CNN: Towards an accurate generic MSK MRI instance segmentation platform (data from the Osteoarthritis Initiative)

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