Automatic segmentation of intracerebral hemorrhage in CT images using encoder–decoder convolutional neural network

Automatic medical image segmentation effectively aids in stroke diagnosis and treatment. In this article, an improved U-Net neural network for auxiliary diagnosis of intracerebral hemorrhage is proposed, which can realize the automatic segmentation of hemorrhage from brain CT images. The pixels of brain CT images are first clustered into four classes: gray matter, white matter, cerebrospinal fluid, and hemorrhage by fuzzy c-means (FCM) clustering, followed by the removal of the skull by morphological imaging, and finally an improved U-Net neural network model is proposed to automatically segment hemorrhages from the brain CT images. Experiment results showed that the objective function of binary cross-entropy was better than dice loss and focal loss for the proposed method. Its dice similarity coefficient reached 0.860 ± 0.031, which was better than the methods of white matter FCM clustering and multipath context generation adversarial networking. This improved method dramatically enhanced the accuracy of segmentation for intracerebral hemorrhage.

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Carpal Bone Segmentation Using Fully Convolutional Neural Network

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190724101600 ◽

2019 ◽

Vol 15 (10) ◽

pp. 983-989

Author(s):

Liang Kim Meng ◽

Azira Khalil ◽

Muhamad Hanif Ahmad Nizar ◽

Maryam Kamarun Nisham ◽

Belinda Pingguan-Murphy ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Automatic Segmentation ◽

Ground Truth ◽

Bone Age ◽

Image Feature ◽

Qualitative Assessment ◽

Carpal Bone ◽

Radiographic Images ◽

Accurate Quantitative Analysis

Background: Bone Age Assessment (BAA) refers to a clinical procedure that aims to identify a discrepancy between biological and chronological age of an individual by assessing the bone age growth. Currently, there are two main methods of executing BAA which are known as Greulich-Pyle and Tanner-Whitehouse techniques. Both techniques involve a manual and qualitative assessment of hand and wrist radiographs, resulting in intra and inter-operator variability accuracy and time-consuming. An automatic segmentation can be applied to the radiographs, providing the physician with more accurate delineation of the carpal bone and accurate quantitative analysis. Methods: In this study, we proposed an image feature extraction technique based on image segmentation with the fully convolutional neural network with eight stride pixel (FCN-8). A total of 290 radiographic images including both female and the male subject of age ranging from 0 to 18 were manually segmented and trained using FCN-8. Results and Conclusion: The results exhibit a high training accuracy value of 99.68% and a loss rate of 0.008619 for 50 epochs of training. The experiments compared 58 images against the gold standard ground truth images. The accuracy of our fully automated segmentation technique is 0.78 ± 0.06, 1.56 ±0.30 mm and 98.02% in terms of Dice Coefficient, Hausdorff Distance, and overall qualitative carpal recognition accuracy, respectively.

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