Using DUCK-Net for polyp image segmentation

Know your orientation: A viewpoint-aware framework for polyp segmentation

Automatic polyp segmentation in endoscopic images is critical for the early diagnosis of colorectal cancer. Despite the availability of powerful segmentation models, two challenges still impede the accuracy of polyp segmentation algorithms. Firstly, during a colonoscopy, physicians frequently adjust the orientation of the colonoscope tip to capture underlying lesions, resulting in viewpoint changes in the colonoscopy images. These variations increase the diversity of polyp visual appearance, posing a challenge for learning robust polyp features. Secondly, polyps often exhibit properties similar to the surrounding tissues, leading to indistinct polyp boundaries. To address these problems, we propose a viewpoint-aware framework named VANet for precise polyp segmentation. In VANet, polyps are emphasized as a discriminative feature and thus can be localized by class activation maps in a viewpoint classification process. With these polyp locations, we design a viewpoint-aware Transformer (VAFormer) to alleviate the erosion of attention by the surrounding tissues, thereby inducing better polyp representations. Additionally, to enhance the polyp boundary perception of the network, we develop a boundary-aware Transformer (BAFormer) to encourage self-attention towards uncertain regions. As a consequence, the combination of the two modules is capable of calibrating predictions and significantly improving polyp segmentation performance. Extensive experiments on seven public datasets across six metrics demonstrate the state-of-the-art results of our method, and VANet can handle colonoscopy images in real-world scenarios effectively. The source code is available at https://github.com/1024803482/Viewpoint-Aware-Network.

PSTNet: Enhanced Polyp Segmentation With Multi-Scale Alignment and Frequency Domain Integration

Accurate segmentation of colorectal polyps in colonoscopy images is crucial for effective diagnosis and management of colorectal cancer (CRC). However, current deep learning-based methods primarily rely on fusing RGB information across multiple scales, leading to limitations in accurately identifying polyps due to restricted RGB domain information and challenges in feature misalignment during multi-scale aggregation. To address these limitations, we propose the Polyp Segmentation Network with Shunted Transformer (PSTNet), a novel approach that integrates both RGB and frequency domain cues present in the images. PSTNet comprises three key modules: the Frequency Characterization Attention Module (FCAM) for extracting frequency cues and capturing polyp characteristics, the Feature Supplementary Alignment Module (FSAM) for aligning semantic information and reducing misalignment noise, and the Cross Perception localization Module (CPM) for synergizing frequency cues with high-level semantics to achieve efficient polyp segmentation. Extensive experiments on challenging datasets demonstrate PSTNet's significant improvement in polyp segmentation accuracy across various metrics, consistently outperforming state-of-the-art methods. The integration of frequency domain cues and the novel architectural design of PSTNet contribute to advancing computer-assisted polyp segmentation, facilitating more accurate diagnosis and management of CRC.

A complete benchmark for polyp detection, segmentation and classification in colonoscopy images

Introduction

Colorectal cancer (CRC) is one of the main causes of deaths worldwide. Early detection and diagnosis of its precursor lesion, the polyp, is key to reduce its mortality and to improve procedure efficiency. During the last two decades, several computational methods have been proposed to assist clinicians in detection, segmentation and classification tasks but the lack of a common public validation framework makes it difficult to determine which of them is ready to be deployed in the exploration room.

Methods

This study presents a complete validation framework and we compare several methodologies for each of the polyp characterization tasks.

Results

Results show that the majority of the approaches are able to provide good performance for the detection and segmentation task, but that there is room for improvement regarding polyp classification.

Discussion

While studied show promising results in the assistance of polyp detection and segmentation tasks, further research should be done in classification task to obtain reliable results to assist the clinicians during the procedure. The presented framework provides a standarized method for evaluating and comparing different approaches, which could facilitate the identification of clinically prepared assisting methods.

Wavelet-based U-shape network for bioabsorbable vascular stents segmentation in IVOCT images

Background and Objective

Coronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use of bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness of current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images is inadequate.

Methods

This paper introduces an enhanced segmentation approach using a novel Wavelet-based U-shape network to address these challenges. We developed a Wavelet-based U-shape network that incorporates an Attention Gate (AG) and an Atrous Multi-scale Field Module (AMFM), designed to enhance the segmentation accuracy by improving the differentiation between the stent struts and the surrounding tissue. A unique wavelet fusion module mitigates the semantic gaps between different feature map branches, facilitating more effective feature integration.

Results

Extensive experiments demonstrate that our model surpasses existing techniques in key metrics such as Dice coefficient, accuracy, sensitivity, and Intersection over Union (IoU), achieving scores of 85.10%, 99.77%, 86.93%, and 73.81%, respectively. The integration of AG, AMFM, and the fusion module played a crucial role in achieving these outcomes, indicating a significant enhancement in capturing detailed contextual information.

Conclusion

The introduction of the Wavelet-based U-shape network marks a substantial improvement in the segmentation of BVSs in IVOCT images, suggesting potential benefits for clinical practices in coronary artery disease treatment. This approach may also be applicable to other intricate medical imaging segmentation tasks, indicating a broad scope for future research.

MGB-Unet: An Improved Multiscale Unet with Bottleneck Transformer for Myositis Segmentation from Ultrasound Images

Myositis is the inflammation of the muscles that can arise from various sources with diverse symptoms and require different treatments. For treatment to achieve optimal results, it is essential to obtain an accurate diagnosis promptly. This paper presents a new supervised segmentation architecture that can efficiently perform precise segmentation and classification of myositis from ultrasound images with few computational resources. The architecture of our model includes a unique encoder-decoder structure that integrates the Bottleneck Transformer (BOT) with a newly developed Residual block named Multi-Conv Ghost switchable bottleneck Residual Block (MCG_RB). This block effectively captures and analyzes ultrasound image input inside the encoder segment at several resolutions. Moreover, the BOT module is a transformer-style attention module designed to bridge the feature gap between the encoding and decoding stages. Furthermore, multi-level features are retrieved using the MCG-RB module, which combines multi-convolution with ghost switchable residual connections of convolutions for both the encoding and decoding stages. The suggested method attains state-of-the-art performance on a benchmark set of myositis ultrasound images across all parameters, including accuracy, precision, recall, dice coefficient, and Jaccard index. Despite its limited training data, the suggested approach demonstrates remarkable generalizability by yielding exceptional results. The proposed model showed a substantial enhancement in accuracy when compared to segmentation state-of-the-art methods such as Unet++, DeepLabV3, and the Duck-Net. The dice coefficient and Jaccard index obtained improvements of up to 3%, 6%, and 7%, respectively, surpassing the other methods.

Deep causal learning for pancreatic cancer segmentation in CT sequences

Segmenting the irregular pancreas and inconspicuous tumor simultaneously is an essential but challenging step in diagnosing pancreatic cancer. Current deep-learning (DL) methods usually segment the pancreas or tumor independently using mixed image features, which are disrupted by surrounding complex and low-contrast background tissues. Here, we proposed a deep causal learning framework named CausegNet for pancreas and tumor co-segmentation in 3D CT sequences. Specifically, a causality-aware module and a counterfactual loss are employed to enhance the DL network's comprehension of the anatomical causal relationship between the foreground elements (pancreas and tumor) and the background. By integrating causality into CausegNet, the network focuses solely on extracting intrinsic foreground causal features while effectively learning the potential causality between the pancreas and the tumor. Then based on the extracted causal features, CausegNet applies a counterfactual inference to significantly reduce the background interference and sequentially search for pancreas and tumor from the foreground. Consequently, our approach can handle deformable pancreas and obscure tumors, resulting in superior co-segmentation performance in both public and real clinical datasets, achieving the highest pancreas/tumor Dice coefficients of 86.67%/84.28%. The visualized features and anti-noise experiments further demonstrate the causal interpretability and stability of our method. Furthermore, our approach improves the accuracy and sensitivity of downstream pancreatic cancer risk assessment task by 12.50% and 50.00%, respectively, compared to experienced clinicians, indicating promising clinical applications.

Overcoming data scarcity in biomedical imaging with a foundational multi-task model

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more specialized datasets common in biomedical imaging. Here we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a universal biomedical pretrained model (UMedPT) on a multi-task database including tomographic, microscopic and X-ray images, with various labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability.

Unveiling camouflaged and partially occluded colorectal polyps: Introducing CPSNet for accurate colon polyp segmentation

BACKGROUND

Segmenting colorectal polyps presents a significant challenge due to the diverse variations in their size, shape, texture, and intricate backgrounds. Particularly demanding are the so-called "camouflaged" polyps, which are partially concealed by surrounding tissues or fluids, adding complexity to their detection.

METHODS

We present CPSNet, an innovative model designed for camouflaged polyp segmentation. CPSNet incorporates three key modules: the Deep Multi-Scale-Feature Fusion Module, the Camouflaged Object Detection Module, and the Multi-Scale Feature Enhancement Module. These modules work collaboratively to improve the segmentation process, enhancing both robustness and accuracy.

RESULTS

Our experiments confirm the effectiveness of CPSNet. When compared to state-of-the-art methods in colon polyp segmentation, CPSNet consistently outperforms the competition. Particularly noteworthy is its performance on the ETIS-LaribPolypDB dataset, where CPSNet achieved a remarkable 2.3% increase in the Dice coefficient compared to the Polyp-PVT model.

CONCLUSION

In summary, CPSNet marks a significant advancement in the field of colorectal polyp segmentation. Its innovative approach, encompassing multi-scale feature fusion, camouflaged object detection, and feature enhancement, holds considerable promise for clinical applications.

Adaptive t-vMF dice loss: An effective expansion of dice loss for medical image segmentation

Dice loss is widely used for medical image segmentation, and many improved loss functions have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equation transformation. Using this knowledge, we present a novel t-vMF Dice loss based on the t-vMF similarity instead of the cosine similarity. Based on the t-vMF similarity, our proposed Dice loss is formulated in a more compact similarity loss function than the original Dice loss. Furthermore, we present an effective algorithm that automatically determines the parameter κ for the t-vMF similarity using a validation accuracy, called Adaptive t-vMF Dice loss. Using this algorithm, it is possible to apply more compact similarities for easy classes and wider similarities for difficult classes, and we are able to achieve adaptive training based on the accuracy of each class. We evaluated binary segmentation datasets of CVC-ClinicDB and Kvasir-SEG, and multi-class segmentation datasets of Automated Cardiac Diagnosis Challenge and Synapse multi-organ segmentation. Through experiments conducted on four datasets using a five-fold cross-validation, we confirmed that the Dice score coefficient (DSC) was further improved in comparison with the original Dice loss and other loss functions.

Unveiling Osteoporosis Through Radiomics Analysis of Hip CT Imaging

RATIONALE AND OBJECTIVES

This study aims to investigate the use of radiomics analysis of hip CT imaging to unveil osteoporosis.

MATERIALS AND METHODS

The researchers analyzed hip CT scans from a cohort of patients, including both osteoporotic and healthy individuals. Radiomics technique are employed to extract a comprehensive array of features from these images, encompassing texture, shape, and intensity alterations. Radiomics analysis using the 10 most commonly used machine learning models was employed to identify screened radiomics features for the detection of osteoporosis in patients. In addition to radiomics features, the basic information of patients is also utilized as training data for these machine learning models to accurately identify the presence of osteoporosis. A comparison would be made between the efficiency of recognizing radiomics features and the efficiency of recognizing patient basic information. The machine learning model that achieves the highest performance would be chosen to integrate patient basic information and radiomics features for the development of clinical nomograms.

RESULT

After a thorough screening process, 16 radiomics features were selected as input parameters for the machine learning model. In the test group, the highest accuracy achieved using radiomics features was 0.849, with an area under the curve (AUC) of 0.919. Evaluation of clinical features identified age and gender as closely associated with osteoporosis. Among these features, the KNN model exhibited the highest accuracy of 0.731 and an AUC of 0.658 in the test group. Comparing the performance of radiomics and clinical features, radiomics features demonstrated superior AUC values in the machine learning models. Ultimately, the XGBoost model, utilizing both radiomics and clinical features, was selected as the final Nomogram prediction model. In the test group, this model achieved an accuracy of 0.882 and an AUC of 0.886 in screening for osteoporosis.

CONCLUSION

Radiomics features derived from hip CT scans exhibit strong screening capabilities for osteoporosis. Furthermore, when combined with easily obtainable clinical features like patient age and gender, an effective screening efficacy for osteoporosis can be achieved.

IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.