Colonoscopy Images Research Articles

Colorectal cancer detection with enhanced precision using a hybrid supervised and unsupervised learning approach

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt. The CVC ClinicDB dataset supports this process, containing 1650 colonoscopy images classified as polyps or non-polyps. The best performance in the ensembles was done by the AD-22 + Transformer + SVM model, with an AUC of 0.99, a training accuracy of 99.50%, and a testing accuracy of 99.00%. This group also saw a high accuracy of 97.50% for Polyps and 99.30% for Non-Polyps, together with a recall of 97.80% for Polyps and 98.90% for Non-Polyps, hence performing very well in identifying both cancerous and healthy regions. The framework proposed here uses K-means clustering in combination with the visualisation of bounding boxes, thereby improving segmentation and yielding a silhouette score of 0.73 with the best cluster configuration. It discusses how to combine feature interpretation challenges into medical imaging for accurate localization and precise segmentation of malignant regions. A good balance between performance and generalization shall be done by hyperparameter optimization-heavy learning rates; dropout rates and overfitting shall be suppressed effectively. The hybrid schema of this work treats the deficiencies of the previous approaches, such as incorporating CNN-based effective feature extraction, Transformer networks for developing attention mechanisms, and finally the fine decision boundary of the support vector machine. Further, we refine this process via unsupervised clustering for the purpose of enhancing the visualisation of such a procedure. Such a holistic framework, hence, further boosts classification and segmentation results by generating understandable outcomes for more rigorous benchmarking of detecting colorectal cancer and with higher reality towards clinical application feasibility.

P0500 Multi-Module Metaformer System for Distinguishing Crohn’s Disease from Intestinal Tuberculosis

Abstract Background Distinguishing Crohn’s Disease (CD) from Intestinal Tuberculosis (ITB) through endoscopy can be particularly challenging, especially for junior physicians or those in resource-limited settings. Accurate differentiation is essential for appropriate treatment and effective patient management. This study aims to develop a multimodal AI-assisted diagnostic model that integrates clinical data and colonoscopy images to reliably distinguish between CD and ITB. Methods Patients diagnosed with CD or ITB at Xiangya Third Hospital of Central South University and Hunan Chest Hospital between January 1, 2005, and August 1, 2023, were included in the study. An external validation set was obtained from Zhuzhou Central Hospital. Data collection involved clinical text information and colonoscopy images for each patient. A multimodal diagnostic model, based on the MetaFormer algorithm, was developed for differential diagnosis, which was evaluated using both internal and external validation sets. Model performance was assessed using accuracy, sensitivity, specificity, and F1 score, and compared to the performance of endoscopists. Results The analysis included 408 patients in the internal dataset (311 with CD and 97 with ITB) and 85 patients in the external validation set (62 with CD and 23 with ITB). The multimodal algorithm outperformed approaches that used a single data type. Compared to state-of-the-art CNN-based algorithms such as VILBERT, BLIP, and CLIP, the multimodal approach demonstrated significant superiority in performance metrics. In the internal dataset, the integrated model achieved 91.25% accuracy, 96.72% sensitivity, and 73.68% specificity, thereby effectively reducing misdiagnosis rates. The robustness of the model was confirmed through external validation, with results showing 91.76% accuracy, 95.16% sensitivity, and 82.61% specificity. The F1 score was calculated as 0.944. The model performance significantly outperforms different levels of endoscopist performance. Junior endoscopists had lower diagnostic accuracy compared to experts (76.24% vs. 85.15%), but using the model to assist junior endoscopists improved their diagnostic accuracy to levels comparable with expert endoscopists (83.17% vs. 85.15%). This indicates the model’s potential to enhance diagnostic accuracy and consistency in clinical practice. Conclusion The MetaFormer-based multimodal predictive model for differentiating CD and ITB demonstrates high discriminatory power, providing an accurate diagnostic tool to support clinical decision-making and improve patient outcomes.

Effectiveness of encoder-decoder deep learning approach for colorectal polyp segmentation in colonoscopy images

Effectiveness of encoder-decoder deep learning approach for colorectal polyp segmentation in colonoscopy images

FocusU2Net: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomatous polyps, thus mitigating the burden of manual image analysis. This study introduces FocusU2Net, an innovative bi-level nested U-structure integrated with a dual-attention mechanism. The model integrates Focus Gate (FG) modules for spatial and channel-wise attention and Residual U-blocks (RSU) with multi-scale receptive fields for capturing diverse contextual information. Comprehensive evaluations on five benchmark datasets - Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETISLarib, and EndoScene - demonstrate Dice score improvements of 3.14% to 43.59% over state-of-the-art models, with an 85% success rate in cross-dataset validations, significantly surpassing prior competing models with sub-5% success rates. The model combines high segmentation accuracy with computational efficiency, featuring 46.64 million parameters, 78.09 GFLOPs, and 39.02 GMacs, making it suitable for real-time applications. Enhanced with Explainable AI techniques, FocusU2Net provides clear insights into its decision-making process, improving interpretability. This combination of high performance, efficiency, and transparency positions FocusU2Net as a powerful, scalable solution for automated polyp segmentation in clinical practice, advancing medical image analysis and computer-aided diagnosis.

AI-enhanced interface for colonic polyp segmentation using DeepLabv3+ with comparative backbone analysis

Polyps are one of the early stages of colon cancer. The detection of polyps by segmentation and their removal by surgical intervention is of great importance for making treatment decisions. Although the detection of polyps through colonoscopy images can lead to multiple expert needs and time losses, it can also include human error. Therefore, automatic, fast, and highly accurate segmentation of polyps from colonoscopy images is important. Many methods have been proposed, including deep learning-based approaches. In this study, a method using DeepLabv3+ with encoder-decoder structure and ResNet architecture as backbone network is proposed for the segmentation of colonic polyps. The Kvasir-SEG polyp dataset was used to train and test the proposed method. After images were preprocessed, the training of the proposed network was performed. The trained network was then tested and performance metrics were calculated, and additionally, a GUI (Graphical User Interface) was designed to enable the segmentation of colonoscopy images for polyp segmentation. The experimental results showed that the ResNet-50 based DeepLabv3+ model had high performance metrics such as DSC: 0.9609, mIoU: 0.9246, demonstrating its effectiveness in the segmentation of colonic polyps. In conclusion, our method utilizing DeepLabv3+ with a ResNet-50 backbone achieves highly accurate colonic polyp segmentation. The obtained results demonstrate its potential to significantly enhance colorectal cancer diagnosis and planning for polypectomy surgery through automated image analysis.&#xD.

ColonNeXt: Fully Convolutional Attention for Polyp Segmentation.

This study introduces ColonNeXt, a novel fully convolutional attention-based model for polyp segmentation from colonoscopy images, aimed at the enhancing early detection of colorectal cancer. Utilizing a purely convolutional neural network (CNN), ColonNeXt integrates an encoder-decoder structure with a hierarchical multi-scale context-aware network (MSCAN) in the encoder and a convolutional block attention module (CBAM) in the decoder. The decoder further includes a proposed CNN-based feature attention mechanism for selective feature enhancement, ensuring precise segmentation. A new refinement module effectively improves boundary accuracy, addressing challenges such as variable polyp size, complex textures, and inconsistent illumination. Evaluations on standard datasets show that ColonNeXt achieves high accuracy and efficiency, significantly outperforming competing methods. These results confirm its robustness and precision, establishing ColonNeXt as a state-of-the-art model for polyp segmentation. The code is available at: https://github.com/long-nguyen12/colonnext-pytorch .

Deep learning-assisted colonoscopy images for prediction of mismatch repair deficiency in colorectal cancer.

Deficient mismatch repair or microsatellite instability is a major predictive biomarker for the efficacy of immune checkpoint inhibitors of colorectal cancer. However, routine testing has not been uniformly implemented due to cost and resource constraints. We developed and validated a deep learning-based classifiers to detect mismatch repair-deficient status from routine colonoscopy images. We obtained the colonoscopy images from the imaging database at Endoscopic Center of the Sixth Affiliated Hospital, Sun Yat-sen University. Colonoscopy images from a prospective trial (Neoadjuvant PD-1 blockade by toripalimab with or without celecoxib in mismatch repair-deficient or microsatellite instability-high locally advanced colorectal cancer) were used to test the model. A total of 5226 eligible images from 892 tumors from the consecutive patients were utilized to develop and validate the deep learning model. 2105 colorectal cancer images from 306 tumors were randomly selected to form model development dataset with a class-balanced approach. 3121 images of 488 proficient mismatch repair tumors and 98 deficient mismatch repair tumors were used to form the independent dataset. The model achieved an AUROC of 0.948 (95% CI 0.919-0.977) on the test dataset. On the independent validation dataset, the AUROC was 0.807 (0.760-0.854), and the NPV in was 94.2% (95% CI 0.918-0.967). On the prospective trial dataset, the model identified 29 tumors among the 33 deficient mismatch repair tumors (87.88%). The model achieved a high NPV in detecting deficient mismatch repair colorectal cancers. This model might serve as an automatic screening tool.

Comparison of YOLO and transformer based tumor detection in cystoscopy

Abstract Background: Bladder cancer (BCa) is the second most common type of cancer in the genitourinary system and causes approximately 165,000 deaths each year. The diagnosis of BCa is primarily done through cystoscopy, which involves visually examining the bladder using an endoscope. Currently, white light cystoscopy is considered the most reliable method for diagnosis. However, it can be challenging to detect and diagnose flat, small, or poorly textured lesions. The study explores the performance of deep learning systems (YOLOv7- tiny, RT-DETR18), originally designed for detecting adenomas in colonoscopy images, when retrained and tested with cystoscopy images. The deep neural network used in the study was pre-trained on 35,699 colonoscopy images (some from Mannheim) and both architectures achieved a F1 score of 0.91 on publicly available colonoscopy datasets. Results: When the adenoma-detection network was tested with cystoscopy images from two sources (Ulm and Erlangen), F1 scores ranging from 0.58 to 0.81 were achieved. Subsequently, the networks were retrained using 12,066 cystoscopy images from Mannheim, resulting in improved F1 scores ranging from 0.77 to 0.85. Conclusion: It could be shown that transformer based networks perform slightly better than YOLOv7-tiny networks, but both network types are feasable for lesion detection in the human bladder. The retraining of the network with additional cystoscopy data led to an improvement in the performance of urinary lesion detection. This suggests that it is possible to achieve a domain-shift with the inclusion of appropriate additional data.

Surgical Insight-guided Deep Learning for Colorectal Lesion Management.

Colonoscopy stands as a pivotal diagnostic tool in identifying gastrointestinal diseases, including potentially malignant tumors. The procedure, however, faces challenges in the precise identification of lesions during visual inspections. The recent strides in AI and machine learning technologies have opened avenues for enhanced medical imaging analysis, including in the field of colonoscopy. In this study, we developed and evaluated a deep learning (DL) model, ColoNet, for detecting lesions in colonoscopic images. We analyzed 1760 images from 306 patients who underwent colorectal surgery between 2009 and 2022, meeting specific inclusion criteria. These images were used to train and validate ColoNet, employing the YOLOv8 architecture and various data augmentation techniques. Deep learning metrics are assessed via YOLO architecture and trained model diagnostic accuracy was assessed via sensitivity, specifity, positive predictive value, and negative predictive value. Our results from the validation dataset revealed a precision of 0.79604, a recall of 0.78086, an mAP50 of 0.83243, and an mAP50-95 of 0.4439. In addition, on a separate real-time dataset of 91 images consisting both healthy and suspect lesions, ColoNet achieved a sensitivity of 70.73%, specificity of 92.00%, positive predictive value (PPV) of 87.88%, and negative predictive value (NPV) of 79.31%. The positive and negative likelihood ratios were 8.84 and 0.32, respectively, with an overall accuracy of 82.42%. In conclusion, our model has demonstrated promising results, indicating its potential as a valuable tool to assist surgeons during colonoscopy procedures. Its ability to detect suspicious lesions with potential malignancy offers a noteworthy advancement in the early diagnosis and management of colorectal cancers. Further multicentric, prospective research and validation are warranted to fully realize its clinical applicability and impact.

Molecular Ultrasound Imaging With Clinically Translatable cRGD-Coated Microbubbles to Assess αvβ3-Integrin Expression in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) subdivides into Crohn disease (CD) and ulcerative colitis (UC), and is characterized by unpredictable periods of inflammation and results in significant patient suffering and even death. Conventional diagnostic methods, for example, colonoscopy, computed tomography, or magnetic resonance imaging, have limitations such as invasiveness, patient discomfort, and limited sensitivity and accuracy. Therefore, we propose ultrasound molecular imaging (USMI) to detect and characterize IBD. First, we evaluated integrin-αvβ3 as a biomarker of IBD in human samples and then used clinically translatable cyclic Arg-Gly-Asp-D-Phe-Lys (cRGDfK)-coupled poly(butyl)cyanoacrylate microbubbles (cRGD-MB) to assess IBD in mice. Vascular integrin-αvβ3 expression in human colon tissue samples (healthy, CD and UC, n = 10 per group) was analyzed by immunofluorescence staining. In mice, acute colitis was induced by administration of 4% dextran sodium sulfate in drinking water for 5 days. On day 7, USMI with cRGD-MB was performed in colitis (n = 6) and healthy (n = 5) mice. The signal of bound cRGD-MB was assessed by the destruction-replenishment method. Ex vivo analysis of mouse colon tissue was performed to assess the degree of colitis by hematoxylin-eosin staining and the vascular expression of integrin-αv by immunofluorescence. Human samples showed a significantly higher vascular integrin-αvβ3 expression in CD and UC tissue, when compared with healthy samples (P < 0.005). In mice, a higher binding of cRGD-MB to inflamed colon was detected by USMI compared with healthy controls (P < 0.005). Immunofluorescence staining confirmed these findings, showing stronger integrin-αv expression in acute colitis, with a good correlation between USMI signal intensity and integrin-αv expression (r = 0.8, P = 0.0016). Integrin-αvβ3 on vessels is a suitable marker for IBD. USMI using cRGD-MB accurately detects this marker and correlates well with histology. These encouraging results support clinical translation of this imaging method as a noninvasive and cost-effective monitoring tool.

PDCA-Net: Parallel dual-channel attention network for polyp segmentation

Accurate segmentation of polyps in colonoscopy images is crucial for the diagnosis and cure of colorectal cancer. Although various deep learning methods have been proposed and have shown promising performance, accurately distinguishing between polyp and mucosal boundaries remains a challenge. In this work, we propose a Parallel Dual-Channel Attention Network (PDCA-Net) for polyp segmentation. This method utilizes the mapping transformations to adaptively encapsulate the global dependency from superpixel into pixels, enhancing the model’s ability to localize foreground and background regions. Specifically, we first design a parallel spatial and channel attention fusion module to capture the global dependencies at the superpixel level from the spatial and channel dimensions. Furthermore, an adaptive associative mapping module is proposed to encapsulate the global dependencies of superpixels into each pixel through a coarse-to-fine learning strategy. Extensive experiments demonstrate that the proposed PDCA-Net effectively improves the segmentation performance and achieves new state-of-the-art results (i.e., 0.815, 0.936, 0.945, and 0.838 mDice, 0.744, 0.891, 0.900, and 0.765 mIoU on the ETIS, Kvasir-SEG, CVC-ClinicDB, and CVC-ColonDB). Our code is available at https://github.com/lzucg/PDCA-Net.

CRCFusionAICADx: Integrative CNN-LSTM Approach for Accurate Colorectal Cancer Diagnosis in Colonoscopy Images

CRCFusionAICADx: Integrative CNN-LSTM Approach for Accurate Colorectal Cancer Diagnosis in Colonoscopy Images

In vivo evaluation of complex polyps with endoscopic optical coherence tomography and deep learning during routine colonoscopy: a feasibility study

Standard-of-care (SoC) imaging for assessing colorectal polyps during colonoscopy, based on white-light colonoscopy (WLC) and narrow-band imaging (NBI), does not have sufficient accuracy to assess the invasion depth of complex polyps non-invasively during colonoscopy. We aimed to evaluate the feasibility of a custom endoscopic optical coherence tomography (OCT) probe for assessing colorectal polyps during routine colonoscopy. Patients referred for endoscopic treatment of large colorectal polyps were enrolled in this pilot clinical study, which used a side-viewing OCT catheter developed for use with an adult colonoscope. OCT images of polyps were captured during colonoscopy immediately before SoC treatment. A deep learning model was trained to differentiate benign from deeply invasive lesions for real-time diagnosis. 35 polyps from 32 patients were included. OCT imaging added on average 3:40 min (range 1:54–8:20) to the total procedure time. No complications due to OCT were observed. OCT revealed distinct subsurface tissue structures that correlated with histological findings, including tubular adenoma (n = 20), tubulovillous adenoma (n = 10), sessile serrated polyps (n = 3), and invasive cancer (n = 2). The deep learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.984 (95%CI 0.972–0.996) and Cohen’s kappa of 0.845 (95%CI 0.774–0.915) when compared to gold standard histopathology. OCT is feasible and safe for polyp assessment during routine colonoscopy. When combined with deep learning, OCT offers clinicians increase confidence in identifying deeply invasive cancers, potentially improving clinical decision-making. Compared to previous studies, ours offers a nuanced comparison between not just benign and malignant lesions, but across multiple histological subtypes of polyps.

Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis.

To date, no studies have evaluated Chat Generative Pre-Trained Transformer (ChatGPT) as a large language model chatbot in optical applications for digestive endoscopy images. This study aimed to weigh the performance of ChatGPT-4 in assessing bowel preparation (BP) quality for colonoscopy. ChatGPT-4 analysed 663 anonymised endoscopic images, scoring each according to the Boston BP scale (BBPS). Expert physicians scored the same images subsequently. ChatGPT-4 deemed 369 frames (62.9%) to be adequately prepared (i.e., BBPS > 1) compared to 524 frames (89.3%) assessed by human assessors. The agreement was slight (κ: 0.099, p = 0.0001). The raw human BBPS score was higher at 3 (2-3) than that of ChatGPT-4 at 2 (1-3), demonstrating moderate concordance (W: 0.554, p = 0.036). ChatGPT-4 demonstrates some potential in assessing BP on colonoscopy images, but further refinement is still needed.

Weighted ensemble deep learning approach for classification of gastrointestinal diseases in colonoscopy images aided by explainable AI

Gastrointestinal diseases are significant health issues worldwide, requiring early diagnosis due to their serious health implications. Therefore, detecting these diseases using artificial intelligence-based medical decision support systems through colonoscopy images plays a critical role in early diagnosis. In this study, a deep learning-based method is proposed for the classification of gastrointestinal diseases and colon anatomical landmarks using colonoscopy images. For this purpose, five different Convolutional Neural Network (CNN) models, namely Xception, ResNet-101, NASNet-Large, EfficientNet, and NASNet-Mobile, were trained. An ensemble model was created using class-based recall values derived from the validation performances of the top three models (Xception, ResNet-101, NASNet-Large). A user-friendly Graphical User Interface (GUI) was developed, allowing users to perform classification tasks and use Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI tool, to visualize the regions from which the model derives information. Grad-CAM visualizations contribute to a better understanding of the model’s decision-making processes and play an important role in the application of explainable AI. In the study, eight labels, including anatomical markers such as z-line, pylorus, and cecum, as well as pathological findings like esophagitis, polyps, and ulcerative colitis, were classified using the KVASIR V2 dataset. The proposed ensemble model achieved a 94.125% accuracy on the KVASIR V2 dataset, demonstrating competitive performance compared to similar studies in the literature. Additionally, the precision and F1 score values ​​of this model are equal to 94.168% and 94.125%, respectively. These results suggest that the proposed method provides an effective solution for the diagnosis of GI diseases and can be beneficial for medical education.

Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.

Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (GITB) is challenging. We aimed to evaluate the clinical applicability of an artificial intelligence (AI) model for this purpose. The AI model was developed and assessed using an internal dataset comprising 1,132 colonoscopy images of CD and 1,045 colonoscopy images of GITB at a tertiary referral center. Its stand-alone performance was further evaluated in an external dataset comprising 67 colonoscopy images of 17 CD patients and 63 colonoscopy images of 14 GITB patients from other institutions. Additionally, a crossover trial involving three expert endoscopists and three trainee endoscopists compared AI-assisted and unassisted human interpretations. In the internal dataset, the sensitivity, specificity, and accuracy of the AI model in distinguishing between CD and GITB were 95.3%, 100.0%, and 97.7%, respectively, with an area under the ROC curve of 0.997. In the external dataset, the AI model exhibited a sensitivity, specificity, and accuracy of 77.8%, 85.1%, and 81.5%, respectively, with an area under the ROC curve of 0.877. In the human endoscopist trial, AI assistance increased the pooled accuracy of the six endoscopists from 86.2% to 88.8% (P=0.010). While AI did not significantly enhance diagnostic accuracy for the experts (96.7% with AI vs 95.6% without, P=0.360), it significantly improved accuracy for the trainees (81.0% vs 76.7%, P=0.002). This AI model shows potential in aiding the accurate differential diagnosis between CD and GITB, particularly benefiting less experienced endoscopists.

Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence.

Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy. Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of the colon's anatomical sections and investigate the relationship of these sections with each other or with age groups. The shapes of the transverse colon were analyzed using artificial intelligence. The study was conducted as a two- and three-dimensional examination of CT colonography images of people between 40 and 80 years old, which were obtained retrospectively. An artificial intelligence algorithm (YOLOv8) was used for shape detection on 3D colon images. 160 people with a mean age of 89 men and 71 women included in the study were 57.79±8.55 and 56.55±6.60, respectively, and there was no statistically significant difference (p= 0.24). The total colon length was 166.11±25.07 cm for men and 158.73±21.92 cm for women, with no significant difference between groups (p=0.12). As a result of the training of the model Precision, Recall, and Mean Average Precision (mAP) were found to be 0.8578, 0.7940, and 0.9142, respectively. The study highlights the importance of understanding the type and morphology of the large intestine for accurate interpretation of CT colonography results and effective clinical management of patients with suspected large intestine abnormalities. Furthermore, this study showed that 88.57% of the images in the test data set were detected correctly and that AI can play an important role in colon typing.

MLFA-UNet: A multi-level feature assembly UNet for medical image segmentation

Medical image segmentation is crucial for accurate diagnosis and treatment in medical image analysis. Among the various methods employed, fully convolutional networks (FCNs) have emerged as a prominent approach for segmenting medical images. Notably, the U-Net architecture and its variants have gained widespread adoption in this domain. This paper introduces MLFA-UNet, an innovative architectural framework aimed at advancing medical image segmentation. MLFA-UNet adopts a U-shaped architecture and integrates two pivotal modules: multi-level feature assembly (MLFA) and multi-scale information attention (MSIA), complemented by a pixel-vanishing (PV) attention mechanism. These modules synergistically contribute to the segmentation process enhancement, fostering both robustness and segmentation precision. MLFA operates within both the network encoder and decoder, facilitating the extraction of local information crucial for accurately segmenting lesions. Furthermore, the bottleneck MSIA module serves to replace stacking modules, thereby expanding the receptive field and augmenting feature diversity, fortified by the PV attention mechanism. These integrated mechanisms work together to boost segmentation performance by effectively capturing both detailed local features and a broader range of contextual information, enhancing both accuracy and resilience in identifying lesions. To assess the versatility of the network, we conducted evaluations of MFLA-UNet across a range of medical image segmentation datasets, encompassing diverse imaging modalities such as wireless capsule endoscopy (WCE), colonoscopy, and dermoscopic images. Our results consistently demonstrate that MFLA-UNet outperforms state-of-the-art algorithms, achieving dice coefficients of 91.42%, 82.43%, 90.8%, and 88.68% for the MICCAI 2017 (Red Lesion), ISIC 2017, PH2, and CVC-ClinicalDB datasets, respectively.

Treatment of chronic sinus tract leakage at rectal anastomosis with anal fistula endoscopy

Objective: To introduce the method of using anal fistula endoscope to treat chronic sinus tract leakage at rectal anastomosis site. Methods: We used anal fistula endoscopy to treat a patient with chronic sinus tract leakage after radical resection of rectal cancer, mainly including the following 5 steps: (1) establishing a water injection circulation system through the anus; (2) scraping off purulent coating and mucosa on the surface of the sinus tract with the brush; (3) hemostasis and removal of necrotic tissue with electrocoagulation rods; (4) filling the sinus tract with bioprotein gel; (5) compressing the sinus tract with transanal drainage tube. Results: The patient is a 70 year old male with rectal cancer. After undergoing 3D laparoscopic assisted radical resection of rectal cancer via abdominal anterior resection (Dixon's procedure) and diverting ileostomy surgery for more than 3 months, leakage of the rectal anastomosis was found through colonoscopy and anal iodine water contrast imaging .The patient started eating and flowing juice 6 hours after surgery, got out of bed 24 hours after surgery, and was discharged 48 hours after the removal of the anal canal. Three months after surgery, colonoscopy and transanal iodine hydrography showed that the sinus repair was intact. The diverting ileostomy was reduced 4 months after surgery. Conclusion: Anal fistula endoscope is safe and feasible for the treatment of chronic sinus tract anastomotic leakage in selected patients.

Highlighted Diffusion Model as Plug-in Priors for Polyp Segmentation.

Automated polyp segmentation from colonoscopy images is crucial for colorectal cancer diagnosis. The accuracy of such segmentation, however, is challenged by two main factors. First, the variability in polyps' size, shape, and color, coupled with the scarcity of well-annotated data due to the need for specialized manual annotation, hampers the efficacy of existing deep learning methods. Second, concealed polyps often blend with adjacent intestinal tissues, leading to poor contrast that challenges segmentation models. Recently, diffusion models have been explored and adapted for polyp segmentation tasks. However, the significant domain gap between RGB-colonoscopy images and grayscale segmentation masks, along with the low efficiency of the diffusion generation process, hinders the practical implementation of these models. To mitigate these challenges, we introduce the Highlighted Diffusion Model Plus (HDM+), a two-stage polyp segmentation framework. This framework incorporates the Highlighted Diffusion Model (HDM) to provide explicit semantic guidance, thereby enhancing segmentation accuracy. In the initial stage, the HDM is trained using highlighted ground-truth data, which emphasizes polyp regions while suppressing the background in the images. This approach reduces the domain gap by focusing on the image itself rather than on the segmentation mask. In the subsequent second stage, we employ the highlighted features from the trained HDM's U-Net model as plug-in priors for polyp segmentation, rather than generating highlighted images, thereby increasing efficiency. Extensive experiments conducted on six polyp segmentation benchmarks demonstrate the effectiveness of our approach.