Abstract
Background: Laparoscopy is a surgery performed in the abdomen without making large incisions in the skin and with the aid of a video camera, resulting in laparoscopic videos. The laparoscopic video is prone to various distortions such as noise, smoke, uneven illumination, defocus blur, and motion blur. One of the main components in the feedback loop of video enhancement systems is distortion identification, which automatically classifies the distortions affecting the videos and selects the video enhancement algorithm accordingly. This paper aims to address the laparoscopic video distortion identification problem by developing fast and accurate multi-label distortion classification using a deep learning model. Current deep learning solutions based on convolutional neural networks (CNNs) can address laparoscopic video distortion classification, but they learn only spatial information. Methods: In this paper, utilization of both spatial and temporal features in a CNN-long short-term memory (CNN-LSTM) model is proposed as a novel solution to enhance the classification. First, pre-trained ResNet50 CNN was used to extract spatial features from each video frame by transferring representation from large-scale natural images to laparoscopic images. Next, LSTM was utilized to consider the temporal relation between the features extracted from the laparoscopic video frames to produce multi-label categories. A novel laparoscopic video dataset proposed in the ICIP2020 challenge was used for training and evaluation of the proposed method. Results: The experiments conducted show that the proposed CNN-LSTM outperforms the existing solutions in terms of accuracy (85%), and F1-score (94.2%). Additionally, the proposed distortion identification model is able to run in real-time with low inference time (0.15 sec). Conclusions: The proposed CNN-LSTM model is a feasible solution to be utilized in laparoscopic videos for distortion identification.
Highlights
Video quality assessment (VQA) in the medical field is an important task to achieve satisfactory conditions for medical imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT) scans, and laparoscopy
Laparoscopic videos are often affected by various types of distortions like noise, smoke, uneven illumination, and blur, which are all concomitant artifacts that arise from operating the laparoscopic surgical equipment.[2]
This paper aims to address the challenge of distortion detection and produce a generic method for distortion classification in laparoscopic videos
Summary
Video quality assessment (VQA) in the medical field is an important task to achieve satisfactory conditions for medical imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT) scans, and laparoscopy. Laparoscopic videos are often affected by various types of distortions like noise, smoke, uneven illumination, and blur, which are all concomitant artifacts that arise from operating the laparoscopic surgical equipment.[2] To enhance the distorted laparoscopic videos, most studies propose solutions that require troubleshooting the equipment.[2,3] such solutions are time consuming and cannot guarantee high-quality laparoscopy every time. This paper aims to address the laparoscopic video distortion identification problem by developing fast and accurate multi-label distortion classification using a deep learning model. Current deep learning solutions based on convolutional neural networks (CNNs) can address laparoscopic video distortion classification, but they learn only spatial information. Methods: In this paper, utilization of both spatial and temporal features in a CNN-long short-term memory (CNN-LSTM) model is proposed as a novel solution to enhance the classification. The proposed distortion identification model is able to run in real-time with low inference time (0.15 sec)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
