A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks

Krenzer, Adrian; Banck, Michael; Makowski, Kevin; Hekalo, A.; Fitting, Daniel; Troya, Joel; Sudarevic, Boban; Zoller, Wolfgang G.; Hann, Alexander; Puppe, Frank

doi:10.3390/jimaging9020026

Cited by 29 publications

(6 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Krenzer et al [11] collected colon images from German hospitals to create their own dataset and developed system called Endomind-advanced for the detection of polyps. They achieved mDice of 92.95% which is 2.08% less than our model's performance.…”

Section: Discussionmentioning

confidence: 99%

“…The inherent difficulty manifests itself when considering the heterogeneous nature of polyps in terms of their size, wherein the polyp may occasionally exhibit a tendency to blend with the surrounding mucous. The quality of colonoscopy images can be influenced by various factors, including the illumination conditions within the lumen, the presence of motion blur, and the specific equipment utilized during the procedure [1,11]. In recent times, there has been notable success in the application of deep learning methodologies to medical imaging tasks, particularly the domain of segmentation.…”

Section: Review Of Literaturesmentioning

confidence: 99%

See 1 more Smart Citation

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract

Selvaraj,

Umapathy

2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Purpose. The objectives of the proposed work are twofold. Firstly, to develop a specialized light weight CRPU-Net for the segmentation of polyps in colonoscopy images. Secondly, to conduct a comparative analysis of the performance of CRPU-Net with implemented state-of-the-art models. Methods. We have utilized two distinct colonoscopy image datasets such as CVC-ColonDB and CVC-ClinicDB. This paper introduces the CRPU-Net, a novel approach for the automated segmentation of polyps in colorectal regions. A comprehensive series of experiments was conducted using the CRPU-Net, and its performance was compared with that of state-of-the-art models such as VGG16, VGG19, U-Net and ResUnet++. Additional analysis such as ablation study, generalizability test and 5-fold cross validation were performed. Results. The CRPU-Net achieved the segmentation accuracy of 96.42% compared to state-of-the-art model like ResUnet++ (90.91%). The Jaccard coefficient of 93.96% and Dice coefficient of 95.77% was obtained by comparing the segmentation performance of the CRPU-Net with ground truth. Conclusion. The CRPU-Net exhibits outstanding performance in Segmentation of polyp and holds promise for integration into colonoscopy devices enabling efficient operation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Review Of Literaturesmentioning

confidence: 99%

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract

Selvaraj,

Umapathy

2023

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

show abstract

“…Further advancements were presented by Krenzer et al 47 , where a real-time polyp detection system utilizing deep convolutional neural networks for clinical application in colonoscopy was developed, showing significant improvements in detection rates and operational efficiency. This development was seen as a crucial step towards the real-time, clinical application of AI in endoscopic procedures, potentially transforming patient outcomes through earlier and more accurate polyp detection.…”

Section: Related Workmentioning

confidence: 99%

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer

Sikkandar,

Sundaram,

Alassaf

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.

show abstract

“…These methods use computer technology to detect polyps called computer-aided detection (CAD). According to this, most of these methods and systems are only research-based and not adequately developed for clinical applications [7]. Therefore, based on these factors, we continuously develop advanced CAD incorporating doctorassisted diagnosis for colon polyp image as an effective medical aid.…”

Section: Introductionmentioning

confidence: 99%

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Chen,

Ahmad

2023

IEEE Access

View full text Add to dashboard Cite

Colorectal cancer has been one of the leading causes of mortality over the past decade, and colorectal polyps are the leading cause of this disease. Conventional polyp detection techniques are insufficient for proper detection; thus, an efficient detection method is indispensable. In this study, we collected colorectal images from a hospital in Taiwan, annotated the ground truth of polyp locations, and integrated them with a public dataset to create a colonoscopy dataset. Data augmentation techniques are further used to increase the training dataset's diversity to improve the models' detection performance. By developing the comparison system based on the recent state-of-the-art methods (i.e., FasterRCNN, SSD, YOLOv3, and YOLOv4), we compared the measurement metrics and statistically analyzed the performance of the models to identify the significant statistical difference in models' performance. Moreover, we developed and integrated error handling mechanism with each model to discard the false and null predictions. Finally, our model comparison system selects and proposes the best performing deep learning model to detect and classify colorectal polyps. We expect that the proposed model will accurately locate and classify different types of polyps. Eventually, this approach will ensure a valuable medical aid model. INDEX TERMSColorectal cancer (CRC), colorectal polyps, polyp detection, deep learning, data augmentation, error handling.

show abstract

A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks

Cited by 29 publications

References 83 publications

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract

CRPU-NET: a deep learning model based semantic segmentation for the detection of colorectal polyp in lower gastrointestinal tract

Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer

Colorectal Polyp Detection and Comparative Evaluation Based on Deep Learning Approaches

Contact Info

Product

Resources

About