Deep Semantic Segmentation of Kidney and Space-occupying Lesion Area Using SCNN and ResNet models combined with SIFT-Flow Algorithm (Preprint)

2018 ◽  
Author(s):  
Xia Kaijian
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Image Features ◽  
Lesion Area ◽  
Abdominal Ct ◽  
Sift Flow ◽  
Kidney Segmentation ◽  
The Impact ◽  
Space Occupying Lesion

BACKGROUND Renal segmentation is one of the most fundamental and challenging tasks in computer aided diagnosis systems. OBJECTIVE In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow transformation is proposed in paper METHODS which is divided into two stages: image retrieval and semantic segmentation. To facilitate the image retrieval, a Metric Learning-based approach is firstly proposed to construct a deep convolutional neural network structure using SCNN and ResNet network to extract image features and minimize the impact of interference factors on features, so as to obtain the ability to represent the abdominal CT scan image with the same angle under different imaging conditions. And then, SIFT Flow transformation is introduced, which adopts MRF to fuse label information, priori spatial information and smoothing information to establish the dense matching relationship of pixels so that the semantics can be transferred from the known image to the target image to obtain the semantic segmentation result of kidney and space-occupying lesion area. RESULTS In order to validate effectiveness and efficiency of our proposed method, we conduct experiments on self-establish CT dataset, focus on kidney organ and most of which have tumors inside of the kidney, and abnormal deformed shape of kidney. The experimental results qualitatively and quantitatively show that the accuracy of kidney segmentation is greatly improved, and the key information of the proportioned tumor occupying a small area of the image are exhibited a good segmentation results. CONCLUSIONS The proposed segmentation algorithm can be effectively applied in clinical diagnosis, help doctors to assist diagnosis, greatly improve the efficiency of work, less error probability.

Deep Semantic Segmentation of Kidney and Space-occupying Lesion Area Using SCNN and ResNet models combined with SIFT-Flow Algorithm (Preprint)

2018 ◽  
Author(s):  
Xia Kaijian
Keyword(s):  
Spatial Information ◽  
Metric Learning ◽  
Semantic Segmentation ◽  
Image Features ◽  
Lesion Area ◽  
Abdominal Ct ◽  
Sift Flow ◽  
Kidney Segmentation ◽  
The Impact ◽  
Space Occupying Lesion

BACKGROUND Renal segmentation is one of the most fundamental and challenging tasks in computer aided diagnosis systems. OBJECTIVE In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow transformation is proposed in paper, METHODS To facilitate the image retrieval, a Metric Learning-based approach is firstly proposed to construct a deep convolutional neural network structure using SCNN and ResNet network to extract image features and minimize the impact of interference factors on features, so as to obtain the ability to represent the abdominal CT scan image with the same angle under different imaging conditions. And then, SIFT Flow transformation is introduced, which adopts MRF to fuse label information, priori spatial information and smoothing information to establish the dense matching relationship of pixels so that the semantics can be transferred from the known image to the target image to obtain the semantic segmentation result of kidney and space-occupying lesion area. RESULTS In order to validate effectiveness and efficiency of our proposed method, we conduct experiments on self-establish CT dataset, focus on kidney organ and most of which have tumors inside of the kidney, and abnormal deformed shape of kidney. The experimental results qualitatively and quantitatively show that the accuracy of kidney segmentation is greatly improved, and the key information of the proportioned tumor occupying a small area of the image are exhibited a good segmentation results. CONCLUSIONS The proposed segmentation algorithm can be effectively applied in clinical diagnosis, help doctors to assist diagnosis, greatly improve the efficiency of work, less error probability.

A COLOR IMAGE RETRIEVAL METHOD BASED ON COLOR MOMENT AND COLOR VARIANCE OF ADJACENT PIXELS

2002 ◽  
Vol 16 (01) ◽  
pp. 113-125 ◽  
Author(s):  
YUNG-KUAN CHAN ◽  
CHIN-CHEN CHANG
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Spatial Information ◽  
Color Image ◽  
Image Features ◽  
Experimental Results ◽  
Retrieval Method ◽  
Color Moment

This paper first introduces three simple and effective image features — the color moment (CM), the color variance of adjacent pixels (CVAP) and CM–CVAP. The CM feature delineates the color-spatial information of images, and the CVAP feature describes the color variance of pixels in an image. However, these two features can only characterize the content of images in different ways. This paper hence provides another feature CM–CVAP, which combines both, to raise the quality of similarity measure. The experimental results show that the image retrieval method based on the CM–CVAP feature gives quite an impressive performance.

scholarly journals Image- vs. histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images

2021 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Talissa A. Altes ◽  
Kun Qing ◽  
Mu He ◽  
G. Wilson Miller ◽  
...  
Keyword(s):  
Spatial Information ◽  
Research Effort ◽  
Semantic Segmentation ◽  
Research Direction ◽  
Image Features ◽  
Evaluation Framework ◽  
Lung Imaging ◽  
Future Research ◽  
Image Domain ◽  
Hyperpolarized Gas

AbstractMagnetic resonance imaging (MRI) using hyperpolarized gases has made possible the novel visualization of airspaces in the human lung, which has advanced research into the growth, development, and pathologies of the pulmonary system. In conjunction with the innovations associated with image acquisition, multiple image analysis strategies have been proposed and refined for the quantification of such lung imaging with much research effort devoted to semantic segmentation, or voxelwise classification, into clinically oriented categories based on ventilation levels. Given the functional nature of these images and the consequent sophistication of the segmentation task, many of these algorithmic approaches reduce the complex spatial image information to intensity-only considerations, which can be contextualized in terms of the intensity histogram. Although facilitating computational processing, this simplifying transformation results in the loss of important spatial cues for identifying salient image features, such as ventilation defects (a well-studied correlate of lung pathophysiology), as spatial objects. In this work, we discuss the interrelatedness of the most common approaches for histogram-based optimization of hyperpolarized gas lung imaging segmentation and demonstrate how certain assumptions lead to suboptimal performance, particularly in terms of measurement precision. In contrast, we illustrate how a convolutional neural network is optimized (i.e., trained) directly within the image domain to leverage spatial information. This image-based optimization mitigates the problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Importantly, we provide the entire processing and evaluation framework, including the newly reported deep learning functionality, as open-source through the well-known Advanced Normalization Tools ecosystem.

scholarly journals A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

2020 ◽  
Vol 9 (2) ◽  
pp. 990-998
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Similarity Measures ◽  
Human Perception ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Future Trends ◽  
Research Issues ◽  
Basic Image ◽  
High Level

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

scholarly journals Impact of Scene Content on High Resolution Video Quality

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2872
Author(s):  
Miroslav Uhrina ◽  
Anna Holesova ◽  
Juraj Bienik ◽  
Lukas Sevcik
Keyword(s):  
Video Compression ◽  
Spatial Information ◽  
Video Quality ◽  
Video Sequences ◽  
Compression Efficiency ◽  
Opinion Score ◽  
Scene Description ◽  
Two Parameters ◽  
The University ◽  
The Impact

This paper deals with the impact of content on the perceived video quality evaluated using the subjective Absolute Category Rating (ACR) method. The assessment was conducted on eight types of video sequences with diverse content obtained from the SJTU dataset. The sequences were encoded at 5 different constant bitrates in two widely video compression standards H.264/AVC and H.265/HEVC at Full HD and Ultra HD resolutions, which means 160 annotated video sequences were created. The length of Group of Pictures (GOP) was set to half the framerate value, as is typical for video intended for transmission over a noisy communication channel. The evaluation was performed in two laboratories: one situated at the University of Zilina, and the second at the VSB—Technical University in Ostrava. The results acquired in both laboratories reached/showed a high correlation. Notwithstanding the fact that the sequences with low Spatial Information (SI) and Temporal Information (TI) values reached better Mean Opinion Score (MOS) score than the sequences with higher SI and TI values, these two parameters are not sufficient for scene description, and this domain should be the subject of further research. The evaluation results led us to the conclusion that it is unnecessary to use the H.265/HEVC codec for compression of Full HD sequences and the compression efficiency of the H.265 codec by the Ultra HD resolution reaches the compression efficiency of both codecs by the Full HD resolution. This paper also includes the recommendations for minimum bitrate thresholds at which the video sequences at both resolutions retain good and fair subjectively perceived quality.

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.

scholarly journals Adversarial Data Augmentation on Breast MRI Segmentation

2021 ◽  
Vol 11 (10) ◽  
pp. 4554
Author(s):  
João F. Teixeira ◽  
Mariana Dias ◽  
Eva Batista ◽  
Joana Costa ◽  
Luís F. Teixeira ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Medical Image Analysis ◽  
Synthetic Data ◽  
Breast Mri ◽  
Semantic Segmentation ◽  
Adversarial Networks ◽  
Augmentation Strategy ◽  
Magnetic Resonance Imaging Mri ◽  
Segmentation Task ◽  
The Impact

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

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