Neural Network Image Segmentation Model for Laparoscopic Gynecological Surgeries

Souza, Cristiano Antonio de; Menegatti, J.E.; Pazello, R.T.; Hajar, Faissal Nemer

doi:10.1016/j.jmig.2021.09.051

Cited by 2 publications

(1 citation statement)

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“…The model performed well with an accuracy of 95%. 29 From the literature review, various segmentation processes have been identified to recognize the aggravation of endometriosis. Segmentation was implemented by detecting features, K-means clustering, supervised machine learning algorithms, and neural network algorithms.…”

Section: Related Studiesmentioning

confidence: 99%

Automated segmentation of endometriosis using transfer learning technique

Visalaxi

SudalaiMuthu

2022

F1000Res

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Background: This paper focuses on segmenting the exact location of endometriosis using the state-of-art technique known as U-Net. Endometriosis is a progressive disorder that has a significant impact on women. The lesion-like appearance that grows inside the uterus and sheds for every periodical cycle is known as endometriosis. If the lesion exists and is transferred to other locations in the women’s reproductive system, it may lead to a serious problem. Besides radiologists deep learning techniques exist for recognizing the presence and aggravation of endometriosis. Methods: The proposed method known as structural similarity analysis of endometriosis (SSAE) identifies the similarity between pathologically identified and annotated images obtained from standardized dataset known as GLENDA v1.5 by implementing two systematic approaches. The first approach is based on semantic segmentation and the second approach uses statistical analysis. Semantic segmentation is a cutting-edge technology for identifying exact locations by performing pixel-level classification. In semantic segmentation, U-Net is a transfer-learning architecture that works effectively for biomedical image classification. The SSAE implements the U-Net architecture for segmenting endometriosis based on the region of occurrence. The second approach proves the similarity between pathologically identified images and the corresponding annotated images using a statistical evaluation. Statistical analysis was performed using calculation of both the mean and standard deviation of all four regions by implementing systematic sampling procedure. Results: The SSAE obtains the intersection over union value of 0.72 and the F1 score of 0.74 for the trained dataset. The means of both the laparoscopic and annotated images for all regions were similar. Consequently, the SSAE facilitated the presence of abnormalities in a specific region. Conclusions: The proposed SSAE approach identifies the affected region using U-Net architecture and systematic sampling procedure.

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Section: Related Studiesmentioning

confidence: 99%