Clinical target segmentation using a novel deep neural network: double attention Res-U-Net

ColonGen: an efficient polyp segmentation system for generalization improvement using a new comprehensive dataset

Colorectal cancer (CRC) is one of the most common causes of cancer-related deaths. While polyp detection is important for diagnosing CRC, high miss rates for polyps have been reported during colonoscopy. Most deep learning methods extract features from images using convolutional neural networks (CNNs). In recent years, vision transformer (ViT) models have been employed for image processing and have been successful in image segmentation. It is possible to improve image processing by using transformer models that can extract spatial location information, and CNNs that are capable of aggregating local information. Despite this, recent research shows limited effectiveness in increasing data diversity and generalization accuracy. This paper investigates the generalization proficiency of polyp image segmentation based on transformer architecture and proposes a novel approach using two different ViT architectures. This allows the model to learn representations from different perspectives, which can then be combined to create a richer feature representation. Additionally, a more universal and comprehensive dataset has been derived from the datasets presented in the related research, which can be used for improving generalizations. We first evaluated the generalization of our proposed model using three distinct training-testing scenarios. Our experimental results demonstrate that our ColonGen-V1 outperforms other state-of-the-art methods in all scenarios. As a next step, we used the comprehensive dataset for improving the performance of the model against in- and out-of-domain data. The results show that our ColonGen-V2 outperforms state-of-the-art studies by 5.1%, 1.3%, and 1.1% in ETIS-Larib, Kvasir-Seg, and CVC-ColonDB datasets, respectively. The inclusive dataset and the model introduced in this paper are available to the public through this link: https://github.com/javadmozaffari/Polyp_segmentation .

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.

Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer

In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomical flexibility, rigidity, and motion within an image deformation, was proposed. Data included 57 CT scans (7202 2D slices) of patients with LACC randomly divided into the train (n = 42) and test (n = 15) datasets. In addition to CT images and the corresponding RT structure (bladder, cervix, and rectum), the bone was segmented, and the coaches were eliminated. The correlated stochastic field was simulated using the same size as the target image (used for deformation) to produce the general random deformation. The deformation field was optimized to have a maximum amplitude in the rectum region, a moderate amplitude in the bladder region, and an amplitude as minimum as possible within bony structures. The DIRNet is a convolutional neural network that consists of convolutional regressors, spatial transformation, as well as resampling blocks. It was implemented by different parameters. Mean Dice indices of 0.89 ± 0.02, 0.96 ± 0.01, and 0.93 ± 0.02 were obtained for the cervix, bladder, and rectum (defined as at-risk organs), respectively. Furthermore, a mean average symmetric surface distance of 1.61 ± 0.46 mm for the cervix, 1.17 ± 0.15 mm for the bladder, and 1.06 ± 0.42 mm for the rectum were achieved. In addition, a mean Jaccard of 0.86 ± 0.04 for the cervix, 0.93 ± 0.01 for the bladder, and 0.88 ± 0.04 for the rectum were observed on the test dataset (15 subjects). Deep learning-based non-rigid image registration is, therefore, proposed for the high-dose-rate brachytherapy in inter-fraction cervical cancer since it outperformed conventional algorithms.