Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Shahedi, Maysam; Halicek, Martin; Dormer, James D.; Schuster, David M.; Fei, Baowei

doi:10.1117/1.jmi.6.2.025003

Cited by 9 publications

(12 citation statements)

References 22 publications

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“…With the rapid development of information technology, a large amount of data has been accumulated in various fields. In the medical field, more and more data sets have been collected, especially medical image data sets [ 1 ]. In recent years, deep learning has made great breakthroughs in the field of natural image processing, which can achieve good results in many tasks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

Lin

2021

Journal of Healthcare Engineering

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In order to deeply study oral three-dimensional cone beam computed tomography (CBCT), the diagnosis of oral and facial surgical diseases based on deep learning was studied. The utility model related to a deep learning-based classification algorithm for oral neck and facial surgery diseases (deep diagnosis of oral and maxillofacial diseases, referred to as DDOM) is brought out; in this method, the DDOM algorithm proposed for patient classification, lesion segmentation, and tooth segmentation, respectively, can effectively process the three-dimensional oral CBCT data of patients and carry out patient-level classification. The segmentation results show that the proposed segmentation method can effectively segment the independent teeth in CBCT images, and the vertical magnification error of tooth CBCT images is clear. The average magnification rate was 7.4%. By correcting the equation of R value and CBCT image vertical magnification rate, the magnification error of tooth image length could be reduced from 7.4. According to the CBCT image length of teeth, the distance R from tooth center to FOV center, and the vertical magnification of CBCT image, the data closer to the real tooth size can be obtained, in which the magnification error is reduced to 1.0%. Therefore, it is proved that the 3D oral cone beam electronic computer based on deep learning can effectively assist doctors in three aspects: patient diagnosis, lesion localization, and surgical planning.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

Lin

2021

Journal of Healthcare Engineering

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“…Comparing the DSC trend (from 81.5% to 90.8% using 0 to 30 input points, respectively) to the average interexpert observer difference in manual prostate segmentation in CT on the same test dataset (∼92%) 13 shows that using minimal user interaction could improve the segmentation results to approach the expert observer performance. The DSC score between the two manual segmentations were measured as the interobserver difference, and the average of DSC scores across the test set was reported as the average interobserver difference.…”

Section: Discussionmentioning

confidence: 96%

“…For this work, we used the hyperparameters from our previous study for prostate segmentation in CT images 13 . We also avoided max pooling along z direction according to our previous observations 13 . Optimizing the hyperparameters of the network to this specific task could improve the results.…”

Section: Discussionmentioning

confidence: 99%

Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks

et al. 2021

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The goal is to study the performance improvement of a deep learning algorithm in three-dimensional (3D) image segmentation through incorporating minimal user interaction into a fully convolutional neural network (CNN). Methods: A U-Net CNN was trained and tested for 3D prostate segmentation in computed tomography (CT) images. To improve the segmentation accuracy, the CNN's input images were annotated with a set of border landmarks to supervise the network for segmenting the prostate. The network was trained and tested again with annotated images after 5, 10, 15, 20, or 30 landmark points were used. Results: Compared to fully automatic segmentation, the Dice similarity coefficient increased up to 9% when 5-30 sparse landmark points were involved, with the segmentation accuracy improving as more border landmarks were used. Conclusions: When a limited number of sparse border landmarks are used on the input image, the CNN performance approaches the interexpert observer difference observed in manual segmentation.

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“…These results were comparable and in general better than other methods reported in the literature. • Shahedi et al developed an automatic, three-dimensional (3-D) prostate segmentation algorithm based on a customized U-Net, which is a type of FCN [35]. In the training phase, abdominal CT images of 69 patients (75% of all images available) were used and applied that of 23 patients (25% of all images) in the test phase.…”

Section: Segmentationmentioning

confidence: 99%

Artificial Intelligence for Medical Imaging and Hygiene

Sugiura

2021

Current Topics in Environmental Health and Preventive Medicine

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Artificial Intelligence (AI) is commonly defined as machine intelligence that is programmed to think like humans and emulate their behavior or simply the technology of developing AI. People receive great benefits from services based on AI technology without even realizing it. One of the technologies that underlie AI is machine learning, and deep learning is a new type of machine learning. Mathematical models of deep learning usually employ a neural network model that imitates the human brain neural network, and a deep neural network has some middle layers to solve the highly difficult issues. A Convolutional Neural Network (CNN), which is a type of deep learning network, can analyze an image directly. In the field of medical imaging and diagnosis, deep learning is already being used to solve numerous difficult issues. Systems have been developed using CNNs and have achieved practical medical application. This chapter focuses in detail on the relationship between deep learning, medical imaging, and hygiene. In particular, it contains an explanation and technical summary of machine learning, the application of deep learning in medical imaging, and the association of the Coronavirus disease 2019 (COVID-19) infection with hygiene and deep learning.

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Deep learning-based three-dimensional segmentation of the prostate on computed tomography images

Cited by 9 publications

References 22 publications

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

Deep Learning-Based Three-Dimensional Oral Conical Beam Computed Tomography for Diagnosis

Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks

Artificial Intelligence for Medical Imaging and Hygiene

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