Real-time Polyp Research Articles

Artificial intelligence based real time colorectal cancer screening study: Polyp segmentation and classification using multi-house database

PurposeThis work explores the potential of artificial intelligence (AI) for real-time colorectal cancer (CRC) screening. We utilized a multi-house dataset to achieve a three-step approach (i) automated polyp segmentation, (ii) analysis of machine learning (ML) for classification and (iii) to compare deep learning (DL) exploration for automated classification. MethodsTo achieve our objectives, we leveraged the CRPU-Net architecture for automated polyp segmentation within the multi-house colonoscopy images. Subsequently, a ML framework was employed for binary classification of the segmented regions. Feature extraction was performed using SIFT and ORB descriptors, followed by the feature fusion and dimensionality reduction through Principal Component Analysis (PCA) to create an optimal feature representation for classification. We further explored the potential of DL by investigating the use of SOTA and vision transformer (ViT) model. Additionally, we considered the development of a hybrid model (CRP-ViT) by potentially combining the ViT model with DL architectures. ResultsCRPU-Net achieved outstanding segmentation performance of 96.56% accuracy, 95.40% IoU, and 97.39% Dice coefficient. Additionally, the CRP-ViT model demonstrated promising results in binary classification of 96.59% accuracy, 96.38% sensitivity, 96.80% specificity, 96.82% precision, and 96.36% NPV. ConclusionThis study implemented CRPU-Net and CRP-ViT, AI systems designed for real-time polyp segmentation, and potential precancerous polyp classification. This comprehensive approach offers the potential to expedite colonoscopies, minimize unnecessary biopsies, improve patient care, and optimize healthcare resource allocation.

D2polyp-Net: A cross-modal space-guided network for real-time colorectal polyp detection and diagnosis

Colorectal polyp screening and identification during endoscopy are crucial for reducing cancer morbidity and patient mortality. Although some deep neural networks for polyp detection and diagnosis have been proposed, there are still problems in small-size polyp localization, poor classification performance especially in white-light endoscopic imaging modality, as well as real-time performance. In this paper, we propose D2polyp-Net, employing a space-guided localization framework and a cross-modal pairwise training strategy, which performs real-time polyp detection and diagnosis simultaneously. The space-guided framework consisting of a dual-pyramid structure builds a fusion bridge between shallow-spatial and deep-semantic information, achieving more accurate localization and providing effective spatial information for polyp classification. The cross-modal pairwise training strategy constrains the output feature of white-light images to approach that of the corresponding narrow-band images, as the latter provide richer mucosal structure and blood flow information to classify polyps. The above modules are validated on publicly available datasets, and D2polyp-Net is demonstrated to surpass other compared state-of-the-art algorithms. It achieves an mAP@0.5 of 0.817 at a frame rate of 28 FPS, with precision and recall above 0.7 for both hyperplastic and adenomatous polyps. Cross-center tests on clinical data further demonstrate the generalization ability of D2polyp-Net, with the mAP@0.5 reaching 0.780. The experimental results show that D2polyp-Net can assist physicians in polyp detection and diagnosis during clinical colonoscopy, which is important for reducing the polyp miss rate and improving the diagnostic efficiency.

MMFIL-Net: Multi-level and multi-source feature interactive lightweight network for polyp segmentation

Accuracy, generalized performance, and model size are critical considerations for designing a real-time polyp segmentation model. However, existing techniques primarily focus on accuracy and do not consider the other two vital characteristics. This paper proposes MMFIL-Net, a novel polyp segmentation technique. As part of MMFIL-Net, the Hierarchical Multi-source Feature Interaction Module (HMFIM) comprises Multi-source Feature Interaction Blocks (MFIB). MFIB manipulates multi-level and multi-sourced features to reduce the gap between low and high-level feature maps to achieve generalized performance. Additionally, the Multiple Receptive Field Feature Interaction Block (MRFFIB) targets the issues of segmenting polyps of different sizes. Finally, Dual Source Attention Fusion Block (DSAFB) is introduced to deal with hazy boundary information for early-stage polyps detection and segmentation. The proposed model outperformed existing lightweight models in conducted evaluation on different datasets. In addition to the achieved generalized performance and higher accuracy, the proposed model presents a significant reduction in the model size than existing approaches. The proposed model only contains 6.68 million parameters and has 4.32G MACs (Multiply–Accumulate Operations), which is better than the current approaches.

MCSF-Net: a multi-scale channel spatial fusion network for real-time polyp segmentation.

Colorectal cancer is a globally prevalent cancer type that necessitates prompt screening. Colonoscopy is the established diagnostic technique for identifying colorectal polyps. However, missed polyp rates remain a concern. Early detection of polyps, while still precancerous, is vital for minimizing cancer-related mortality and economic impact. In the clinical setting, precise segmentation of polyps from colonoscopy images can provide valuable diagnostic and surgical information. Recent advances in computer-aided diagnostic systems, specifically those based on deep learning techniques, have shown promise in improving the detection rates of missed polyps, and thereby assisting gastroenterologists in improving polyp identification. In the present investigation, we introduce MCSF-Net, a real-time automatic segmentation framework that utilizes a multi-scale channel space fusion network. The proposed architecture leverages a multi-scale fusion module in conjunction with spatial and channel attention mechanisms to effectively amalgamate high-dimensional multi-scale features. Additionally, a feature complementation module is employed to extract boundary cues from low-dimensional features, facilitating enhanced representation of low-level features while keeping computational complexity to a minimum. Furthermore, we incorporate shape blocks to facilitate better model supervision for precise identification of boundary features of polyps. Our extensive evaluation of the proposed MCSF-Net on five publicly available benchmark datasets reveals that it outperforms several existing state-of-the-art approaches with respect to different evaluation metrics. The proposed approach runs at an impressive ∼45 FPS, demonstrating notable advantages in terms of scalability and real-time segmentation.

TransResU-Net: A Transformer based ResU-Net for Real-Time Colon Polyp Segmentation.

Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high. Early detection of polyps at the precancerous stage can help reduce the mortality rate and the economic burden associated with colorectal cancer. Deep learning-based computer-aided diagnosis (CADx) system may help gastroenterologists to identify polyps that may otherwise be missed, thereby improving the polyp detection rate. Additionally, CADx system could prove to be a cost-effective system that improves long-term colorectal cancer prevention. In this study, we proposed a deep learning-based architecture for automatic polyp segmentation called Transformer ResU-Net (TransResU-Net). Our proposed architecture is built upon residual blocks with ResNet-50 as the backbone and takes advantage of the transformer self-attention mechanism as well as dilated convolution(s). Our experimental results on two publicly available polyp segmentation benchmark datasets showed that TransResU-Net obtained a highly promising dice score and a real-time speed. With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer. The source code of the proposed TransResU-Net is publicly available at https://github.com/nikhilroxtomar/TransResUNet.

Artificial Intelligence in Colonoscopy.

Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett's esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy. Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy. AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.

Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Endometrial polyps are common gynecological lesions. The standard treatment for this condition is hysteroscopic polypectomy. However, this procedure may be accompanied by misdetection of endometrial polyps. To improve the diagnostic accuracy and reduce the risk of misdetection, a deep learning model based on YOLOX is proposed to detect endometrial polyps in real time. Group normalization is employed to improve its performance with large hysteroscopic images. In addition, we propose a video adjacent-frame association algorithm to address the problem of unstable polyp detection. Our proposed model was trained on a dataset of 11,839 images from 323 cases provided by a hospital and was tested on two datasets of 431 cases from two hospitals. The results show that the lesion-based sensitivity of the model reached 100% and 92.0% for the two test sets, compared with 95.83% and 77.33%, respectively, for the original YOLOX model. This demonstrates that the improved model may be used effectively as a diagnostic tool during clinical hysteroscopic procedures to reduce the risk of missing endometrial polyps.

Robust real-time polyp detection system design based on YOLO algorithms by optimizing activation functions and hyper-parameters with artificial bee colony (ABC)

Colorectal cancer (CRC) is one of the most common cancer types with a high mortality rate. Colonoscopy is considered the gold standard in CRC screening, it also provides immediate removal of polyps, which are the precursors of CRC, significantly reducing CRC mortality. Polyps can be overlooked due to many factors and can progress to a fatal stage. Increasing the detection rate of missed polyps can be a turning point for CRC. Therefore, many traditional computer-aided detection (CAD) systems have been proposed, but the desired efficiency could not be obtained due to real-time detection or the limited sensitivity and specificity of the systems. In this article, we present a deep learning-based approach unlike traditional systems. This approach is basically based on 5th version of you only look once (YOLOv5) object detection algorithm and artificial bee colony (ABC) optimization algorithm. While models belonging to the YOLOv5 algorithm are used for polyp detection, the ABC algorithm is used to improve the performance of the models. The ABC algorithm is positioned to find the optimal activation functions and hyper-parameters for the YOLOv5 algorithm. The proposed method was performed on the novel Showa University and Nagoya University polyp database (SUN) dataset and PICCOLO white-light and narrow-band imaging colonoscopic dataset (PICCOLO). Experimental studies showed that the ABC algorithm successfully optimizes the YOLOv5 algorithm and offers much higher accuracy than the original YOLOv5 algorithm. The proposed method is far ahead of the existing methods in the literature in terms of speed and accuracy, with high performance in real-time polyp detection. This study is the first proposed method for optimization of activation functions and hyper-parameters for object detection algorithms.

FRCNet: Feature Refining and Context-Guided Network for Efficient Polyp Segmentation.

Colorectal cancer, also known as rectal cancer, is one of the most common forms of cancer, and it can be completely cured with early diagnosis. The most effective and objective method of screening and diagnosis is colonoscopy. Polyp segmentation plays a crucial role in the diagnosis and treatment of diseases related to the digestive system, providing doctors with detailed auxiliary boundary information during clinical analysis. To this end, we propose a novel light-weight feature refining and context-guided network (FRCNet) for real-time polyp segmentation. In this method, we first employed the enhanced context-calibrated module to extract the most discriminative features by developing long-range spatial dependence through a context-calibrated operation. This operation is helpful to alleviate the interference of background noise and effectively distinguish the target polyps from the background. Furthermore, we designed the progressive context-aware fusion module to dynamically capture multi-scale polyps by collecting multi-range context information. Finally, the multi-scale pyramid aggregation module was used to learn more representative features, and these features were fused to refine the segmented results. Extensive experiments on the Kvasir, ClinicDB, ColonDB, ETIS, and Endoscene datasets demonstrated the effectiveness of the proposed model. Specifically, FRCNet achieves an mIoU of 84.9% and mDice score of 91.5% on the Kvasir dataset with a model size of only 0.78 M parameters, outperforming state-of-the-art methods. Models and codes are available at the footnote. 1

A Machine Learning-Based System for Real-Time Polyp Detection (DeFrame): A Retrospective Study.

Background and AimsRecent studies have shown that artificial intelligence-based computer-aided detection systems possess great potential in reducing the heterogeneous performance of doctors during endoscopy. However, most existing studies are based on high-quality static images available in open-source databases with relatively small data volumes, and, hence, are not applicable for routine clinical practice. This research aims to integrate multiple deep learning algorithms and develop a system (DeFrame) that can be used to accurately detect intestinal polyps in real time during clinical endoscopy.MethodsA total of 681 colonoscopy videos were collected for retrospective analysis at Xiangya Hospital of Central South University from June 2019 to June 2020. To train the machine learning (ML)-based system, 6,833 images were extracted from 48 collected videos, and 1,544 images were collected from public datasets. The DeFrame system was further validated with two datasets, consisting of 24,486 images extracted from 176 collected videos and 12,283 images extracted from 259 collected videos. The remaining 198 collected full-length videos were used for the final test of the system. The measurement metrics were sensitivity and specificity in validation dataset 1, precision, recall and F1 score in validation dataset 2, and the overall performance when tested in the complete video perspective.ResultsA sensitivity and specificity of 79.54 and 95.83%, respectively, was obtained for the DeFrame system for detecting intestinal polyps. The recall and precision of the system for polyp detection were determined to be 95.43 and 92.12%, respectively. When tested using full colonoscopy videos, the system achieved a recall of 100% and precision of 80.80%.ConclusionWe have developed a fast, accurate, and reliable DeFrame system for detecting polyps, which, to some extent, is feasible for use in routine clinical practice.

Weakly Supervised Polyp Segmentation in Colonoscopy Images Using Deep Neural Networks.

Colorectal cancer (CRC) is a leading cause of mortality worldwide, and preventive screening modalities such as colonoscopy have been shown to noticeably decrease CRC incidence and mortality. Improving colonoscopy quality remains a challenging task due to limiting factors including the training levels of colonoscopists and the variability in polyp sizes, morphologies, and locations. Deep learning methods have led to state-of-the-art systems for the identification of polyps in colonoscopy videos. In this study, we show that deep learning can also be applied to the segmentation of polyps in real time, and the underlying models can be trained using mostly weakly labeled data, in the form of bounding box annotations that do not contain precise contour information. A novel dataset, Polyp-Box-Seg of 4070 colonoscopy images with polyps from over 2000 patients, is collected, and a subset of 1300 images is manually annotated with segmentation masks. A series of models is trained to evaluate various strategies that utilize bounding box annotations for segmentation tasks. A model trained on the 1300 polyp images with segmentation masks achieves a dice coefficient of 81.52%, which improves significantly to 85.53% when using a weakly supervised strategy leveraging bounding box images. The Polyp-Box-Seg dataset, together with a real-time video demonstration of the segmentation system, are publicly available.

Real-time diagnostic accuracy of blue light imaging, linked color imaging and white-light endoscopy for colorectal polyp characterization.

Background and study aims Fujifilm has developed a novel ELUXEO 7000 endoscope system that employs light-emitting diodes (LEDs) at four different wavelengths as light sources that enable blue light imaging (BLI), linked color imaging (LCI), and high-definition white-light endoscopy (HD-WLE). The aim of this study was to address the diagnostic accuracy of real-time polyp characterization using BLI, LCI and HD-WLE (ELUXEO 7000 endoscopy system). Patients methods This is a prespecified post-hoc analysis of a prospective study in which 22 experienced endoscopists (> 2,000 colonoscopies) from eight international centers participated. Using a combination of BLI, LCI, and HD-WLE, lesions were endoscopically characterized including a high- or low-confidence statement. Per protocol, digital images were created from all three imaging modalities. Histopathology was the reference standard. Endoscopists were familiar with polyp characterization, but did not take dedicated training for purposes of this study. Results Overall, 341 lesions were detected in 332 patients. Of the lesions, 269 histologically confirmed polyps with an optical diagnosis were included for analysis (165 adenomas, 27 sessile serrated lesions, and 77 hyperplastic polyps). Overall, polyp characterization was performed with high confidence in 82.9 %. The overall accuracy for polyp characterization was 75.1 % (95 % confidence interval [CI] 69.5–80.1 %), compared with an accuracy of 78.0 % (95 % CI 72.0–83.2 %) for high confidence assignments. The accuracy for endoscopic characterization for diminutive polyps was 74.7 % (95 %CI 68.4–80.3 %), compared with an accuracy of 78.2 % (95 % CI 71.4–84.0 %) for high-confidence assignments. Conclusions The diagnostic accuracy of BLI, LCI, and HD-WLE by experienced endoscopist for real-time polyp characterization seems limited (NCT03344289).

Real-time polyp detection model using convolutional neural networks

Colorectal cancer is a major health problem, where advances towards computer-aided diagnosis (CAD) systems to assist the endoscopist can be a promising path to improvement. Here, a deep learning model for real-time polyp detection based on a pre-trained YOLOv3 (You Only Look Once) architecture and complemented with a post-processing step based on an object-tracking algorithm to reduce false positives is reported. The base YOLOv3 network was fine-tuned using a dataset composed of 28,576 images labelled with locations of 941 polyps that will be made public soon. In a frame-based evaluation using isolated images containing polyps, a general F1 score of 0.88 was achieved (recall = 0.87, precision = 0.89), with lower predictive performance in flat polyps, but higher for sessile, and pedunculated morphologies, as well as with the usage of narrow band imaging, whereas polyp size < 5 mm does not seem to have significant impact. In a polyp-based evaluation using polyp and normal mucosa videos, with a positive criterion defined as the presence of at least one 50-frames-length (window size) segment with a ratio of 75% of frames with predicted bounding boxes (frames positivity), 72.61% of sensitivity (95% CI 68.99–75.95) and 83.04% of specificity (95% CI 76.70–87.92) were achieved (Youden = 0.55, diagnostic odds ratio (DOR) = 12.98). When the positive criterion is less stringent (window size = 25, frames positivity = 50%), sensitivity reaches around 90% (sensitivity = 89.91%, 95% CI 87.20–91.94; specificity = 54.97%, 95% CI 47.49–62.24; Youden = 0.45; DOR = 10.76). The object-tracking algorithm has demonstrated a significant improvement in specificity whereas maintaining sensitivity, as well as a marginal impact on computational performance. These results suggest that the model could be effectively integrated into a CAD system.

Artificial intelligence and polyp detection in colonoscopy: Use of a single neural network to achieve rapid polyp localization for clinical use.

Artificial intelligence has been extensively studied to assist clinicians in polyp detection, but such systems usually require expansive processing power, making them prohibitively expensive and hindering wide adaption. The current study used a fast object detection algorithm, known as the YOLOv3 algorithm, to achieve real-time polyp detection on a laptop. In addition, we evaluated and classified the causes of false detections to further improve accuracy. The YOLOv3 algorithm was trained and validated with 6038 and 2571 polyp images, respectively. Videos from live colonoscopies in a tertiary center and those obtained from public databases were used for the training and validation sets. The algorithm was tested on 10 unseen videos from the CVC-Video ClinicDB dataset. Only bounding boxes with an intersection over union area of >0.3 were considered positive predictions. Polyp detection rate in our study was 100%, with the algorithm able to detect every polyp in each video. Sensitivity, specificity, and F1 score were 74.1%, 85.1%, and 83.3, respectively. The algorithm achieved a speed of 61.2frames per second (fps) on a desktop RTX2070 GPU and 27.2fps on a laptop GTX2060 GPU. Nearly a quarter of false negatives happened when the polyps were at the corner of an image. Image blurriness accounted for approximately 3% and 9% of false positive and false negative detections, respectively. The YOLOv3 algorithm can achieve real-time poly detection with high accuracy and speed on a desktop GPU, making it low cost and accessible to most endoscopy centers worldwide.

A robust real-time deep learning based automatic polyp detection system

Colorectal cancer (CRC) is globally the third most common type of cancer. Colonoscopy is considered the gold standard in colorectal cancer screening and allows for the removal of polyps before they become cancerous. Computer-aided detection systems (CADs) have been developed to detect polyps. Unfortunately, these systems have limited sensitivity and specificity. In contrast, deep learning architectures provide better detection by extracting the different properties of polyps. However, the desired success has not yet been achieved in real-time polyp detection. Here, we propose a new structure for real-time polyp detection by scaling the YOLOv4 algorithm to overcome these obstacles. For this, we first replace the whole structure with Cross Stage Partial Networks (CSPNet), then substitute the Mish activation function for the Leaky ReLu activation function and also substituted the Distance Intersection over Union (DIoU) loss for the Complete Intersection over Union (CIoU) loss. We improved performance of YOLOv3 and YOLOv4 architectures using different structures such as ResNet, VGG, DarkNet53, and Transformers. To increase success of the proposed method, we utilized a variety of data augmentation approaches for preprocessing, an ensemble learning model, and NVIDIA TensorRT for post processing. In order to compare our study with other studies more objectively, we only employed public data sets and followed MICCAI Sub-Challenge on Automatic Polyp Detection in Colonoscopy. The proposed method differs from other methods with its real-time performance and state-of-the-art detection accuracy. The proposed method (without ensemble learning) achieved higher results than those found in the literature, precision: 91.62%, recall: 82.55%, F1-score: 86.85% on public ETIS-LARIB data set and precision: 96.04%, recall: 96.68%, F1-score: 96.36% on public CVC-ColonDB data set, respectively.

Identifies Polyps in Real Time With Accuracy 96.67% in Screening Colonoscopy Using Convolutional Neural Networks (CNN)

Identifies Polyps in Real Time With Accuracy 96.67% in Screening Colonoscopy Using Convolutional Neural Networks (CNN)

Precise Yet Efficient Semantic Calibration and Refinement in ConvNets for Real-time Polyp Segmentation from Colonoscopy Videos

We propose a novel convolutional neural network (ConvNet) equipped with two new semantic calibration and refinement approaches for automatic polyp segmentation from colonoscopy videos. While ConvNets set state-of-the-are performance for this task, it is still difficult to achieve satisfactory results in a real-time manner, which is a necessity in clinical practice. The main obstacle is the huge semantic gap between high-level features and low-level features, making it difficult to take full advantage of complementary semantic information contained in these hierarchical features. Compared with existing solutions, which either directly aggregate these features without considering the semantic gap or employ sophisticated non-local modeling techniques to refine semantic information by introduce many extra computational costs, the proposed ConvNet is able to more precisely yet efficiently calibrate and refine semantic information for better segmentation performance without increasing model complexity; we call the proposed ConvNet as SCR-Net, which has two key modules. We first propose a semantic calibration module (SCM) to effectively transmit the semantic information from high-level layers to low-level layers by learning the semantic-spatial relations during the training procedure. We then propose a semantic refinement module (SRM) to, based on the features calibrated by SCM, enhance the discrimination capability of the features for targeting objects. Extensive experiments on the Kvasir-SEG dataset demonstrate that the proposed SCR-Net is capable of achieving better segmentation accuracy than state-of-the-art approaches with a faster speed. The proposed techniques are general enough to be applied to similar applications where precise and efficient multi-level feature fusion is critical. The code is available at https://github.com/jiafuz/SCR-Net.

Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations.

Background and study aims Several computer-assisted polyp detection systems have been proposed, but they have various limitations, from utilizing outdated neural network architectures to a requirement for multi-graphics processing unit (GPU) processing, to validating on small or non-robust datasets. To address these problems, we developed a system based on a state-of-the-art convolutional neural network architecture able to detect polyps in real time on a single GPU and tested on both public datasets and full clinical examination recordings. Methods The study comprised 165 colonoscopy procedure recordings and 2678 still photos gathered retrospectively. The system was trained on 81,962 polyp frames in total and then tested on footage from 42 colonoscopies and CVC-ClinicDB, CVC-ColonDB, Hyper-Kvasir, and ETIS-Larib public datasets. Clinical videos were evaluated for polyp detection and false-positive rates whereas the public datasets were assessed for F1 score. The system was tested for runtime performance on a wide array of hardware. Results The performance on public datasets varied from an F1 score of 0.727 to 0.942. On full examination videos, it detected 94 % of the polyps found by the endoscopist with a 3 % false-positive rate and identified additional polyps that were missed during initial video assessment. The system’s runtime fits within the real-time constraints on all but one of the hardware configurations. Conclusions We have created a polyp detection system with a post-processing pipeline that works in real time on a wide array of hardware. The system does not require extensive computational power, which could help broaden the adaptation of new commercially available systems.

Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy.

Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology. The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately. We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance. The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83. Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.

Super resolution convolutional neural network based pre-processing for automatic polyp detection in colonoscopy images

Colonoscopy is the most common methodology used to detect polyps on the colon surface. Increasing the image resolution has the potential to improve the automatic colonoscopy based diagnosis and polyp detection and localization. In this study, we proposed a pre-processing approach that uses convolutional neural network based super resolution method (SRCNN) to increase the resolution of the training colonoscopy images before the localization of polyps. We also investigated the use of CNN based models such as the Single Shot MultiBox Detector (SSD) and Faster Regional CNN (RCNN) for real-time polyp detection and localization. Our results showed that using SRCNN method before the training process provides better results in terms of accuracy in both models compared to the low-resolution cases. Furthermore, we reached an F2 score of 0.945 for the correct localization of colon polyps using Faster RCNN with ResNet-101 feature extractor.