AM-SegNet for additive manufacturing
            <i>in situ</i>
            X-ray image segmentation and feature quantification

Li, Wei; Lambert-Garcia, Rubén; Getley, Anna C. M.; Kim, Kwan; Bhagavath, Shishira; Majkut, Marta; Rack, Alexander; Lee, Peter D.; Leung, Chu Lun Alex

doi:10.1080/17452759.2024.2325572

Cited by 17 publications

(7 citation statements)

References 48 publications

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“…These innovations could potentially enhance the performance of EndoAdd, particularly in challenging endoscopic environments where real-time processing and accurate detection are paramount. Moreover, the adoption of transformer-based models like DETR may offer new possibilities for handling the sequential nature of endoscopic video data, leveraging self-attention mechanisms for improved feature recognition and localization [18].…”

Section: Discussionmentioning

confidence: 99%

“…Current AI models, such as convolutional neural networks (CNNs) integrated with long short-term memory (LSTM) networks and lightweight neural networks, have shown promising results in identifying operative phases in endoscopic procedures with high accuracy [18]. Yamazaki et al developed a system based on the YOLOv3 platform to detect and classify surgical instruments in laparoscopic gastrectomy videos.…”

Section: Introductionmentioning

confidence: 99%

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An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video)

Zhu,

Du,

et al. 2024

Bioengineering

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Background and Aims: Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images. Methods: Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos. Results: EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses. Conclusions: We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video)

Zhu,

Du,

et al. 2024

Bioengineering

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“…In the field of AM, researchers continually develop innovative tools to ensure the quality of manufactured components. Li et al [69] introduced AM-SegNet, a deep learning model designed to rapidly and accurately segment high-resolution synchrotron X-ray images in metal AM. This model achieved an impressive 96% accuracy, with processing times below four milliseconds per frame, allowing for efficient identification and analysis of critical features like keyholes and pores, thereby advancing our understanding of AM processes.…”

Section: Importance Of Defect Analysis For Quality Controlmentioning

confidence: 99%

Enhancing Quality Control: Image-Based Quantification of Carbides and Defect Remediation in Binder Jetting Additive Manufacturing

Choudhari,

Elder,

Mugale

et al. 2024

Materials

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While binder jetting (BJ) additive manufacturing (AM) holds considerable promise for industrial applications, defects often compromise part quality. This study addresses these challenges by investigating binding mechanisms and analyzing common defects, proposing tailored solutions to mitigate them. Emphasizing defect identification for effective quality control in BJ-AM, this research offers strategies for in-process rectification and post-process evaluation to elevate part quality. It shows how to successfully process metallic parts with complex geometries while maintaining consistent material properties. Furthermore, the paper explores the microstructure of AISI M2 tool steel, utilizing advanced image processing techniques like digital image analysis and SEM images to evaluate carbide distribution. The results show that M2 tool steel has a high proportion of M6C carbides, with furnace-cooled samples ranging from ~2.4% to 7.1% and MC carbides from ~0.4% to 9.4%. M6C carbides ranged from ~2.6% to 3.8% in air-cooled samples, while water-cooled samples peaked at ~8.52%. Sintering conditions also affected shrinkage, with furnace-cooled samples showing the lowest rates (1.7 ± 0.4% to 5 ± 0.4%) and water-cooled samples showing the highest (2 ± 0.4% to 14.1 ± 0.4%). The study recommends real-time defect detection systems with autonomous corrective capabilities to improve the quality and performance of BJ-AM components.

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“…The U-Net architecture can take a pre-trained encoder and use it during training. U-Net architecture is widely used for semantic segmentation tasks, since the architecture can be modified and improved through different operations quite easily, which can lead to new network architectures based on U-Net such as those seen in [35,36]. For our research, the EfficientNet-b3 [37] encoder was selected and used for the multi-class and binary segmentation networks in this case.…”

Section: U-net Deep Convolutional Networkmentioning

confidence: 99%

Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks

Serban,

Kupas,

Hajdu

et al. 2024

Sensors

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Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only visual information is available and they cannot use their tactile senses during keyhole surgeries. This is the case with laparoscopic hysterectomy since some organs are also difficult to distinguish based on visual information, making laparoscope-based hysterectomy challenging. In this paper, we propose a solution based on semantic segmentation, which can create pixel-accurate predictions of surgical images and differentiate the uterine arteries, ureters, and nerves. We trained three binary semantic segmentation models based on the U-Net architecture with the EfficientNet-b3 encoder; then, we developed two ensemble techniques that enhanced the segmentation performance. Our pixel-wise ensemble examines the segmentation map of the binary networks on the lowest level of pixels. The other algorithm developed is a region-based ensemble technique that takes this examination to a higher level and makes the ensemble based on every connected component detected by the binary segmentation networks. We also introduced and trained a classic multi-class semantic segmentation model as a reference and compared it to the ensemble-based approaches. We used 586 manually annotated images from 38 surgical videos for this research and published this dataset.

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AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification

Cited by 17 publications

References 48 publications

An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video)

An Automated Video Analysis System for Retrospective Assessment and Real-Time Monitoring of Endoscopic Procedures (with Video)

Enhancing Quality Control: Image-Based Quantification of Carbides and Defect Remediation in Binder Jetting Additive Manufacturing

Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks

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