scholarly journals T-MIS: Transparency Adaptation in Medical Image Segmentation

2021 ◽  
Vol 1 (1) ◽  
pp. 11-13
Author(s):  
Ayush Somani ◽  
Divij Singh ◽  
Dilip Prasad ◽  
Alexander Horsch
Keyword(s):  
Image Features ◽  
Medical Image Segmentation ◽  
Fine Tuning ◽  
User Interactions ◽  
Training Samples ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Segmentation Task ◽  
Unseen Objects ◽  
Medical Segmentation

We often locate ourselves in a trade-off situation between what is predicted and understanding why the predictive modeling made such a prediction. This high-risk medical segmentation task is no different where we try to interpret how well has the model learned from the image features irrespective of its accuracy. We propose image-specific fine-tuning to make a deep learning model adaptive to specific medical imaging tasks. Experimental results reveal that: a) proposed model is more robust to segment previously unseen objects (negative test dataset) than state-of-the-art CNNs; b) image-specific fine-tuning with the proposed heuristics significantly enhances segmentation accuracy; and c) our model leads to accurate results with fewer user interactions and less user time than conventional interactive segmentation methods. The model successfully classified ’no polyp’ or ’no instruments’ in the image irrespective of the absence of negative data in training samples from Kvasir-seg and Kvasir-Instrument datasets.

scholarly journals A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Shiyong Ji ◽  
Benzheng Wei ◽  
Zhen Yu ◽  
Gongping Yang ◽  
Yilong Yin
Keyword(s):  
Fuzzy Clustering ◽  
Brain Mri ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Imaging Characteristic ◽  
Multi Stage ◽  
Segmentation Accuracy ◽  
Medical Segmentation ◽  
Influence Of Noise ◽  
Mri Image

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image.

scholarly journals Instance Segmentation of Microscopic Foraminifera

2021 ◽  
Author(s):  
Thomas Haugland Johansen ◽  
Steffen Aagaard Sørensen ◽  
Kajsa Møllersen ◽  
Fred Godtliebsen
Keyword(s):  
Counting Process ◽  
Detection Task ◽  
Fine Tuning ◽  
Average Precision ◽  
Novel Object ◽  
Proposed Model ◽  
Model Weight ◽  
Segmentation Task ◽  
Future Work ◽  
Instance Segmentation

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in e.g. paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of 0.78±0.00 on the classification and detection task, and 0.80±0.00 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84±0.00 and 0.86±0.00, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work, and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera.

scholarly journals Deep Neural Network-Based Semantic Segmentation of Microvascular Decompression Images

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1167
Author(s):  
Ruifeng Bai ◽  
Shan Jiang ◽  
Haijiang Sun ◽  
Yifan Yang ◽  
Guiju Li
Keyword(s):  
Semantic Segmentation ◽  
Fine Tuning ◽  
Cerebral Vessel ◽  
Current State ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Spatial Pyramid Pooling ◽  
Boundary Information ◽  
True Color ◽  
Good Research

Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial nerve segmentation based on true-color medical images is in urgent need and has good research and development prospects. We have extended the current state-of-the-art semantic-segmentation network DeepLabv3+ and used it as the basic framework. First, the feature distillation block (FDB) was introduced into the encoder structure to refine the extracted features. In addition, the atrous spatial pyramid pooling (ASPP) module was added to the decoder structure to enhance the retention of feature and boundary information. The proposed model was trained by fine tuning and optimizing the relevant parameters. Experimental results show that the encoder structure has better performance in feature refinement processing, improving target boundary segmentation precision, and retaining more feature information. Our method has a segmentation accuracy of 75.73%, which is 3% better than DeepLabv3+.

scholarly journals Instance Segmentation of Microscopic Foraminifera

2021 ◽  
Vol 11 (14) ◽  
pp. 6543
Author(s):  
Thomas Haugland Johansen ◽  
Steffen Aagaard Sørensen ◽  
Kajsa Møllersen ◽  
Fred Godtliebsen
Keyword(s):  
Counting Process ◽  
Detection Task ◽  
Fine Tuning ◽  
Average Precision ◽  
Novel Object ◽  
Proposed Model ◽  
Model Weight ◽  
Segmentation Task ◽  
Future Work ◽  
Instance Segmentation

Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in paleo-oceanographic and -climatological research. However, the identification and counting process has been performed manually since the 1800s and is laborious and time-consuming. In this work, we present a deep learning-based instance segmentation model for classifying, detecting, and segmenting microscopic foraminifera. Our model is based on the Mask R-CNN architecture, using model weight parameters that have learned on the COCO detection dataset. We use a fine-tuning approach to adapt the parameters on a novel object detection dataset of more than 7000 microscopic foraminifera and sediment grains. The model achieves a (COCO-style) average precision of 0.78 on the classification and detection task, and 0.80 on the segmentation task. When the model is evaluated without challenging sediment grain images, the average precision for both tasks increases to 0.84 and 0.86, respectively. Prediction results are analyzed both quantitatively and qualitatively and discussed. Based on our findings we propose several directions for future work and conclude that our proposed model is an important step towards automating the identification and counting of microscopic foraminifera.

scholarly journals Segmenting Medical MRI via Recurrent Decoding Cell

2020 ◽  
Vol 34 (07) ◽  
pp. 12452-12459 ◽  
Author(s):  
Ying Wen ◽  
Kai Xie ◽  
Lianghua He
Keyword(s):  
Feature Fusion ◽  
Image Features ◽  
Medical Image Segmentation ◽  
Network Robustness ◽  
Feature Maps ◽  
Segmentation Accuracy ◽  
Model Size ◽  
Score Map ◽  
A Chain

The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. An encoder-decoder network, named Convolutional Recurrent Decoding Network (CRDN), is also proposed based on RDC for segmenting multi-modality medical MRI. CRDN adopts CNN backbone to encode image features and decode them hierarchically through a chain of RDCs to obtain the final high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and HVSMR datasets demonstrate that the introduction of RDC effectively improves the segmentation accuracy as well as reduces the model size, and the proposed CRDN owns its robustness to image noise and intensity non-uniformity in medical MRI.

Medical image segmentation using boundary-enhanced guided packet rotation dual attention decoder network

2021 ◽  
pp. 1-15
Author(s):  
Hongchun Lu ◽  
Shengwei Tian ◽  
Long Yu ◽  
Yan Xing ◽  
Junlong Cheng ◽  
...  
Keyword(s):  
Medical Images ◽  
Automatic Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Feature Maps ◽  
Deep Feature ◽  
Proposed Model ◽  
Segmentation Accuracy ◽  
Feature Information ◽  
Deep Learning Network

OBJECTIVE: The automatic segmentation of medical images is an important task in clinical applications. However, due to the complexity of the background of the organs, the unclear boundary, and the variable size of different organs, some of the features are lost during network learning, and the segmentation accuracy is low. To address these issues, This prompted us to study whether it is possible to better preserve the deep feature information of the image and solve the problem of low segmentation caused by unclear image boundaries. METHODS: In this study, we (1) build a reliable deep learning network framework, named BGRANet,to improve the segmentation performance for medical images; (2) propose a packet rotation convolutional fusion encoder network to extract features; (3) build a boundary enhanced guided packet rotation dual attention decoder network, which is used to enhance the boundary of the segmentation map and effectively fuse more prior information; and (4) propose a multi-resolution fusion module to generate high-resolution feature maps. We demonstrate the effffectiveness of the proposed method on two publicly available datasets. RESULTS: BGRANet has been trained and tested on the prepared dataset and the experimental results show that our proposed model has better segmentation performance. For 4 class classifification (CHAOS dataset), the average dice similarity coeffiffifficient reached 91.73%. For 2 class classifification (Herlev dataset), the prediction, sensitivity, specifificity, accuracy, and Dice reached 93.75%, 94.30%, 98.19%, 97.43%, and 98.08% respectively. The experimental results show that BGRANet can improve the segmentation effffect for medical images. CONCLUSION: We propose a boundary-enhanced guided packet rotation dual attention decoder network. It achieved high segmentation accuracy with a reduced parameter number.

FINE-TUNING OF MODELLING STRATEGY TO SIMULATE THERMO-MECHANICAL BEHAVIOUR OF DOUBLE FRICTION PENDULUM SEISMIC ISOLATORS UST ESTIMATOR

2019 ◽  
Vol 3 (Special Issue on First SACEE'19) ◽  
pp. 165-172
Author(s):  
Vincenzo Bianco ◽  
Giorgio Monti ◽  
Nicola Pio Belfiore
Keyword(s):  
Multibody Dynamics ◽  
Mechanical Behaviour ◽  
Mechanical Model ◽  
Dynamic Behaviour ◽  
Experimental Testing ◽  
Fine Tuning ◽  
Proposed Model ◽  
Modelling Techniques ◽  
Friction Pendulum

The use of friction pendulum devices has recently attracted the attention of both academic and professional engineers for the protection of structures in seismic areas. Although the effectiveness of these has been shown by the experimental testing carried out worldwide, many aspects still need to be investigated for further improvement and optimisation. A thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented in this paper. The proposed model is based on the observation that sliding may not take place as ideally as is indicated in the literature. On the contrary, the fulfilment of geometrical compatibility between the constitutive bodies (during an earthquake) suggests a very peculiar dynamic behaviour composed of a continuous alternation of sticking and slipping phases. The thermo-mechanical model of a double friction pendulum device (based on the most recent modelling techniques adopted in multibody dynamics) is presented. The process of fine-tuning of the selected modelling strategy (available to date) is also described.

scholarly journals Dense cellular segmentation for EM using 2D–3D neural network ensembles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Matthew D. Guay ◽  
Zeyad A. S. Emam ◽  
Adam B. Anderson ◽  
Maria A. Aronova ◽  
Irina D. Pokrovskaya ◽  
...  
Keyword(s):  
Blood Platelets ◽  
Semantic Segmentation ◽  
3D Models ◽  
Image Features ◽  
Cell Models ◽  
Whole Cells ◽  
Large Numbers ◽  
The Creation ◽  
Segmentation Task ◽  
Neural Network Ensembles

AbstractBiologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has been a major factor limiting insight into the complex variability of cellular environments. Modern EM can produce gigavoxel image volumes containing large numbers of cells, but accurate manual segmentation of image features is slow and limits the creation of cell models. Segmentation algorithms based on convolutional neural networks can process large volumes quickly, but achieving EM task accuracy goals often challenges current techniques. Here, we define dense cellular segmentation as a multiclass semantic segmentation task for modeling cells and large numbers of their organelles, and give an example in human blood platelets. We present an algorithm using novel hybrid 2D–3D segmentation networks to produce dense cellular segmentations with accuracy levels that outperform baseline methods and approach those of human annotators. To our knowledge, this work represents the first published approach to automating the creation of cell models with this level of structural detail.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

scholarly journals Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 1934 ◽  
Author(s):  
Bao-Di Liu ◽  
Wen-Yang Xie ◽  
Jie Meng ◽  
Ye Li ◽  
Yanjiang Wang
Keyword(s):  
Remote Sensing ◽  
Test Sample ◽  
Remote Sensing Image ◽  
Image Features ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Great Success ◽  
Specific Class ◽  
Remote Sensing Images ◽  
Training Samples

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

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