Query: Mask Rcnn

title: Adapting Mask RCNN for Automatic Nucleus Segmentation
abstract: Automatic segmentation of microscopy images is an important task in medical image processing and analysis. Nucleus detection is an important example of this task. Mask RCNN is a recently proposed state of the art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper we demonstrate that Mask RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions.

title: Fruit detection for strawberry harvesting robot in non structural environment based on Mask RCNN
abstract: Deep learning has demonstrated excellent capabilities for learning image features and is widely used in image object detection. In order to improve the performance of machine vision in fruit detection for a strawberry harvesting robot, Mask Region Convolutional Neural Network (Mask RCNN) was introduced. Resnet50 was adopted as backbone network, combined with the Feature Pyramid Network (FPN) architecture for feature extraction. The Region Proposal Network (RPN) was trained end to end to create region proposals for each feature map. After generating mask images of ripe fruits from Mask R CNN, a visual localization method for strawberry picking points was performed. Fruit detection results of 100 test images showed that the average detection precision rate was 95.78% the recall rate was 95.41% and the mean intersection over union MIoU rate for instance segmentation was 89.85% The prediction results of 573 ripe fruit picking points showed that the average error was 1.2 mm. Compared with four traditional methods, the method proposed demonstrates improved universality and robustness in a non structural environment, particularly for overlapping and hidden fruits, and those under varying illumination.

title: Mask RCNN and U Net Ensembled for Nuclei Segmentation
abstract: Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open source software have made these tools easier to implement, expert knowledge is still required to choose the right model architecture and training setup. We compare two popular segmentation frameworks, U Net and Mask RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.

title: Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask RCNN
abstract: Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp MRI can be subjective, development of computer aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask RCNN model to segment the prostate and ILs. We trained and evaluated models on 120 patients from two different cohorts of patients. We also used 2D U Net and 3D U Net as benchmarks to segment the prostate and compared the model's performance. The contour variability of ILs using the algorithm was also benchmarked against the interobserver variability between two different radiation oncologists on 19 patients. Our results indicate that the Mask RCNN model is able to reach state of art performance in the prostate segmentation and outperforms several competitive baselines in ILs segmentation.

title: Instance Based Segmentation for Boundary Detection of Neuropathic Ulcers Through Mask RCNN
abstract: Neuropathic ulcers form and proliferate because of peripheral neuropathy, usually in diabetic patients. The existing ulcer assessment process which relies on visual examination, potentially be imprecise and inefficient. Therefore this indicates the necessity of a more quantitative and cost effective solution that enables ulcer diagnosing process much faster. In the current literature, different deep learning approaches are available for diagnosing illnesses through medical imagery. When diagnosing diabetic patients who are suffering from neuropathic ulcers through imagery, the locating and segmenting of ulcer boundaries is of great importance. In this study, we propose an approach to automate the process of locating and segmenting ulcers through Mask RCNN model. We use a dataset of 400 ulcer imagery and corresponding annotations of ulcers for this task. This approach achieves an overall ulcer detection average precision (AP) at Intersection over union (IoU) threshold 0.5 of 0.8632 and mean average precision (mAP) at Intersection over union (IoU) threshold 0.5 to 0.95 by steps of size 0.05 of 0.5084 for ResNet 101 backbone.

title: Automatic Nucleus Segmentation with Mask RCNN
abstract: Mask RCNN is a recently proposed state of the art algorithm for object detection, object localization, and object instance segmentation of natural images. In this paper, it is demonstrated that Mask RCNN can be used to perform highly effective and efficient automatic segmentations of a wide range of microscopy images of cell nuclei, for a variety of cells acquired under a variety of conditions. In addition, it is shown that a cyclic learning rate regime allows effective training of a Mask RCNN model without any need to finetune the learning rate, thereby eliminating a manual and time consuming aspect of the training procedure. The results presented here will be of interest to those in the medical imaging field and to computer vision researchers more generally.

title: A Mask RCNN Baseline for Probabilistic Object Detection
abstract: The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability based Detection Quality (PDQ) on a new synthetic image dataset. We present our submission to the challenge, a fine tuned version of Mask RCNN with some additional post processing. Our method, submitted under username pammirato, is currently second on the leaderboard with a score of 21.432, while also achieving the highest spatial quality and average overall quality of detections. We hope this method can provide some insight into how detectors designed for mean average precision (mAP) evaluation behave under PDQ, as well as a strong baseline for future work.

title: A Value Recognition Algorithm for Pointer Meter Based on Improved Mask RCNN
abstract: The automatic reading recognition technology for pointer meter is widely used in the military, industrial, and aerospace fields. However, the accuracy of recognizing pointer meter is susceptible to some factors during the recognition process, such as uneven illumination, complex background, rotation angle, image blur, shooting angle, scale change and other factors, all of which may result in lower accuracy and usability. In this paper, in order to overcome these difficulties, we make improvements to the existing Mask RCNN network, and propose a novel deep learning based algorithm using PrRoIPooling instead of RoiAlign to achieve the accurate segmentation to improve the accuracy of meter readings. The main procedure of the algorithm is to first use the Mask RCNN instance segmentation network to segment the meter dial and the pointer area, and then classify and recognize the meter type while fitting the pointer. Finally, the angle reading method is used to calculate the pointer reading. Experiments show that the algorithm is robust, adaptable and effective to the natural environment.

title: A MASK RCNN Based Approach Using Scale Invariant Feature Transform Key points for Object Detection from Uniform Background Scene
abstract: Object identification using deep learning in known environment gives a new dimension to the research area of computer vision based automation system. As it uses supervised learning technique using Convolution Neural Network (RCNN) it helps automation software tools and machines to detect and identify objects using vision based systems. One of RCNN technique known as Mask RCNN has been applied in this proposed design and this paper presents a novel approach to object detection problem using Big Data storage for large set of features based data.  Region based CNN has led to the development of a state of the art object detector termed as Mask R CNN. Some samples of solid material objects used in refractory industry have been taken as input images. In our experiment the SIFT based features have been implemented and trained using filter and convolution operation. In addition to improved accuracy, pixel level annotation (annotating bounding boxes is approximately an order of magnitude which is quicker to perform) The model is retrained to perform the detection of four types of metal objects, with the entire process of annotation and training for the new model per solid block. A key benefit of feature based Mask RCNN approach is high precision ~94% in classification and minimized feature points with SIFT key points.

title: Automatic knee meniscus tear detection and orientation classification with Mask RCNN.
abstract: This work presents our contribution to a data challenge organized by the French Radiology Society during the Journees Francophones de Radiologie in October 2018. This challenge consisted in classifying MR images of the knee with respect to the presence of tears in the knee menisci, on meniscal tear location, and meniscal tear orientation.  We trained a mask region based convolutional neural network (R CNN) to explicitly localize normal and torn menisci, made it more robust with ensemble aggregation, and cascaded it into a shallow ConvNet to classify the orientation of the tear.  Our approach predicted accurately tears in the database provided for the challenge. This strategy yielded a weighted AUC score of 0.906 for all three tasks, ranking first in this challenge.  The extension of the database or the use of 3D data could contribute to further improve the performances especially for non typical cases of extensively damaged menisci or multiple tears.