Co-Mask R-CNN: collaborative learning-based method for tooth instance segmentation.

Fusion of Mask RCNN and attention mechanism for instance segmentation of apples under complex background

We explored a Mask Region-based Convolutional Neural Network (Mask R-CNN) to detect macrophages in quantitative phase images, which were acquired by digital holographic microscopy (DHM), an interferometry-based variant of quantitative phase imaging (QPI). The Mask R-CNN deep learning architecture is capable to detect and segment single macrophage cells in quantitative phase images and allows to perform both tasks in a multi-stage process. Our results show that the combined detection and segmentation of cells through Mask R-CNN-based automated evaluation prospects a fast and robust screening in label-free high throughput microscopy.

This study explores the integration of deep learning techniques for predictive maintenance within urban rail systems, emphasizing advanced automated rail head wear detection utilizing Mask Region-based Convolutional Neural Network (Mask R-CNN) and You Only Look Once (YOLO)v8 models. The increasing traffic and operational demands, especially in regions like Pakistan, amplify challenges such as rolling contact fatigue and head checks. This study showcases the effectiveness of the Mask R-CNN and YOLOv8 models, with Mask R-CNN achieving an average Intersection over Union of 0.8694 and a Dice Coefficient of 0.9300, and YOLOv8m attaining an average Intersection over Union of 0.9564 and a Dice Coefficient of 0.9777. These findings underscore the potential of these models to significantly enhance rail maintenance protocols and contribute to the sustainability and safety of intelligent urban rail transportation systems.

The impact damage is one of the major causes of structural failures in Fiber Metal Laminate (FML) plates, which are widely used in the aerospace and automotive industries due to their superior mechanical properties. Accurate detection, segmentation, and characterization of these damages are crucial for improved safety and reduced maintenance costs. This study proposes an automated approach to detect, segment, reconstruct, and characterize the damages in FML plates using state-of-the-art deep learning models: the Segment Anything Model (SAM) and the Mask Region-based Convolutional Neural Network (Mask R-CNN) implemented by the Detectron2 framework. A domain-adapted supervised learning process was applied to the X-ray CT dataset of damaged FML plates impacted with energies of 5J, 7.5J, 10J, and 12.5J. Mask R-CNN significantly outperformed SAM across all key performance metrics while offering around 8 times faster training and 80 times faster inference. Mask R-CNN also proved to have superior explainability for end-users. The lack of absolute ground truth data severely limits the scope of an absolute quantitative comparison, therefore highlighting the need for further studies. This study not only contributes to the area of damage diagnostics in composite materials but also provides insights into the comparative performance and explainability of advanced deep learning models, paving the way for applications in industrial inspection and quality assurance.

In this study, a lightweight phenotyping system that combined the advantages of both deep learning-based panicle detection and the photogrammetry based on light consumer-level UAVs was proposed. A two-year experiment was conducted to perform data collection and accuracy validation. A deep learning model, named Mask Region-based Convolutional Neural Network (Mask R-CNN), was trained to detect panicles in complex scenes of paddy fields. A total of 13 857 images were fed into Mask R-CNN, with 80% used for training and 20% used for validation. Scores, precision, recall, Average Precision (AP), and F1-score of the Mask R-CNN, were 82.46%, 80.60%, 79.46%, and 79.66%, respectively. A complete workflow was proposed to preprocess flight trajectories and remove repeated detection and noises. Eventually, the evident changed in rice growth during the heading stage was visualized with geographic distributions, and the total number of panicles was predicted before harvest. The average error of the predicted amounts of panicles was 33.98%. Experimental results showed the feasibility of using the developed system as the high-throughput phenotyping approach. Keywords: panicle detection, vision-based phenotyping, deep learning, unmanned aerial vehicle (UAV) DOI: 10.25165/j.ijabe.20211401.6025 Citation: Lyu S X, Noguchi N, Ospina R, Kishima Y. Development of phenotyping system using low altitude UAV imagery and deep learning. Int J Agric & Biol Eng, 2021; 14(1): 207–215.

Wide-scale automatic monitoring based on the Normalized Difference Water Index (NDWI) and Mask Region-based Convolutional Neural Network (Mask R-CNN) with remote sensing images is of great significance for the management of aquaculture areas. However, different spatial resolutions brought different cost and model performance. To find more suitable image spatial resolutions for automatic monitoring offshore aquaculture areas, seven different resolution remote sensing images in the Sandu’ao area of China, from 2 m, 4 m, to 50 m, were compared. Results showed that the remote sensing images with a resolution of 15 m and above can achieve the corresponding recognition effect when no financial issues were considered, with the F1 score of over 0.75. By establishing a cost-effectiveness evaluation formula that comprehensively considers image price and recognition effect, the best image resolution in different scenes can be found, thus providing the most appropriate data scheme for the automatic monitoring of offshore aquaculture areas.

In order to solve the problems of low efficiency, small scale, and data reliability in the traditional manual counting method, a method using mask region convolutional neural network (Mask R-CNN) to automatically calculate attendance was proposed. In order to extract deeper image information, the feature extraction network is designed as ResNet101, and feature map fusion is performed on multi-level feature maps. In order to make up for the lack of recognition of the objects whose body parts are occluded, the Mask R-CNN algorithm is used for the second recognition. The experimental results on the self-built classroom monitoring screenshot data set show that compared with the method of directly using the Mask R-CNN algorithm for recognition, the secondary recognition method can identify more targets and improve the accuracy of identifying the number of people.

The quantitative measurement of intestinal crypts morphology and their architecture can provide valuable insights on the normal intestinal functioning and on subtle changes linked to disease onset and progression. To ensure accurate measurement, the identification and accurate delineation of the crypts boundaries is paramount for the extraction of morphometrics features that then need to be linked to clinical assessment. Endocytoscopy enables the virtual histology and real time assessment of mucosa with its high magnification capability to visualize the crypts and other microstructure. We used a Mask Region-based Convolutional Neural Network (Mask R-CNN), to localize and segment crypts, to extract the quantitative morphometric index which may potentially be useful to characterize patients with healthy and inflamed mucosa in Ulcerative Colitis (UC). A total of 65 endocytoscopy videos from 47 patients are processed for segmentation followed by quantitative measurements of the crypts. For the Mask R-CNN segmentation process, on test data of four patients, no false positive is detected, and sensitivity observed is 94% with overall accuracy of 96%.Then, from all frames with crypts segmentation, we extract parameters such as crypt density, area, eccentricity, diameter, average distance of the crypt from the neighboring crypts. We observed that the automatic measurements have a 95% correlation with those extracted from the manually annotated crypts.Clinical Relevance- Quantitative measurements of intestinal crypts are important for understanding gut health, disease pathogenesis, and the response to therapies. Example: In colorectal cancer, abnormal crypt growth is a key early feature hence this approach can aid in detecting early-stage cancer.

Oversized vehicles have the potential to collide with walls or ceilings when passing through tunnels and bridges, posing a serious threat to the health of transportation infrastructure and public safety. Hence, it is crucial to accurately and immediately detect vehicle dimensions, including length, width, and height, to avoid such accidents. Using computer vision and view geometry, this study presents a framework for automatically detecting and quantifying the outer contours of vehicles through monocular vision. First, traffic scene images are captured which are then used to create a transformation matrix of the ground surface. Second, a modified Mask region‐based convolutional neural network (Mask R‐CNN) is constructed to detect and segment the vehicle instances from the video frames. Finally, a view geometry‐based algorithm was developed to detect the outer contours of passing vehicles and quantify their dimensions. In the field test, the accuracy of the vehicle segmentation and the identified vehicle dimensions was validated. In addition, the proposed method’s superiority was confirmed by comparing it with two other existing approaches. The comparison results show that the proposed method has better accuracy and is more convenient to use since it does not require a premeasured reference. In addition, the developed method can accurately identify not only the dimensions of vehicles parallel to the road but also vehicles that are changing lanes or making a U‐turn.

Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.

This research employs machine learning (Mask Region-Based Convolutional Neural Networks [Mask R-CNN]) and cluster analysis (Density-based spatial clustering of applications with noise [DBSCAN]) to identify more than 20,000 relict charcoal hearths (RCHs) organized in large “fields” within and around State Game Lands (SGLs) in Pennsylvania. This research has two important threads that we hope will advance the archaeological study of landscapes. The first is the significant historical impact of charcoal production, a poorly understood industry of the late eighteenth to early twentieth century, on the historic and present landscape of the United States. Although this research focuses on charcoal production in Pennsylvania, it has broad application for both identifying and contextualizing historical charcoal production throughout the world and for better understanding modern charcoal production. The second thread is the use of open data, open source, and open access tools to conduct this analysis, as well as the open publication of the resultant data. Not only does this research demonstrate the significance of open access tools and data but the open publication of our code as well as our data allow others to replicate our work, to tweak our code and protocols for their own work, and reuse our results.

<title>Abstract</title> <bold>Objectives</bold> Tooth numbering plays a significant role in dental care. The aim of this study was to evaluate the Mask Region-based Convolutional Neural Network (Mask R-CNN) model for tooth number detection, classification, and segmentation from smartphone photographs. <bold>Materials and Methods</bold> This retrospective study utilized the data collected in Finland during 2022. A total of 448 individuals, aged 13–78 years, participated in this study. Participants uploaded dental photographs taken by smartphone. The dataset consisted of upper and lower occlusal, right and left lateral and frontal views. Teeth were labelled using polygon annotation for the FDI World Dental Federation (FDI) notation. The final number of images and tooth polygons were 1,272 and 14,736 respectively. The Mask R-CNN model was used for tooth number recognition. Performance of trained models was measured based on the bounding box and class prediction for 28 tooth numbers, using PASCAL performance metric’s open toolset. The mean average precision (mAP) was calculated for each class with an intersection over union (IoU) threshold of 0.5. External validation was conducted using open Mendeley dataset from India, consisting of 40 preliminary selected images. <bold>Results</bold> The tooth number recognition model demonstrated an overall mAP of 88.2%, with a standard deviation of 0.0075 on 10-fold cross validation, which indicates stable performance of the model. The external validation showed excellent performance of the model, with a mAP of 84.1%. <bold>Conclusions</bold> The Mask R-CNN model developed in this study demonstrated excellent performance and good generalizability. Clinical Relevance: The developed mHealth system facilitates remote dental screening in real-world community settings.

Deep learning-based online image analysis of the water suspension granulation process for 1,3,5-trinitro-1,3,5-triazacyclohexane-based polymer-bonded explosives

Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce. Manual detection of blight disease can be cumbersome and may require trained experts. To overcome these issues, we present an automated system using the Mask Region-based convolutional neural network (Mask R-CNN) architecture, with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions. The approach uses transfer learning, which can generate good results even with small datasets. The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day. The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf. The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches, which can confound the outcome of binary classification. To improve the detection performance, the original RGB dataset was then converted to HSL, HSV, LAB, XYZ, and YCrCb color spaces. A separate model was created for each color space and tested on 417 field-based test images. This yielded 81.4% mean average precision on the LAB model and 56.9% mean average recall on the HSL model, slightly outperforming the original RGB color space model. Manual analysis of the detection performance indicates an overall precision of 98% on leaf images in a field environment containing complex backgrounds.

Accurate assessment of pressure injuries (PIs) is necessary for a good outcome. Junior and non-specialist nurses have less experience with PIs and lack clinical practice, and so have difficulty staging them accurately. In this work, a deep learning-based system for PI staging and tissue classification is proposed to help improve its accuracy and efficiency in clinical practice, and save healthcare costs. A total of 1610 cases of PI and their corresponding photographs were collected from clinical practice, and each sample was accurately staged and the tissues labelled by experts for training a Mask Region-based Convolutional Neural Network (Mask R-CNN, Facebook Artificial Intelligence Research, Meta, US) object detection and instance segmentation network. A recognition system was set up to automatically stage and classify the tissues of the remotely uploaded PI photographs. On a test set of 100 samples, the average precision of this model for stage recognition reached 0.603, which exceeded that of the medical personnel involved in the comparative evaluation, including an enterostomal therapist. In this study, the deep learning-based PI staging system achieved the evaluation performance of a nurse with professional training in wound care. This low-cost system could help overcome the difficulty of identifying PIs by junior and non-specialist nurses, and provide valuable auxiliary clinical information.