scholarly journals Segmentation of Liver Anatomy by Combining 3D U-Net Approaches

2021 ◽  
Vol 11 (11) ◽  
pp. 4895
Author(s):  
Abir Affane ◽  
Adrian Kucharski ◽  
Paul Chapuis ◽  
Samuel Freydier ◽  
Marie-Ange Lebre ◽  
...  
Keyword(s):  
State Of The Art ◽  
Liver Volume ◽  
Vessel Segmentation ◽  
Liver Segmentation ◽  
Crucial Importance ◽  
Liver Anatomy ◽  
Hepatic Diseases ◽  
Hepatic Vessels ◽  
Segmentation Task ◽  
Vessel Reconstruction

Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.

State of the Art on Carbonic Anhydrase Modulators for Biomedical Purposes

2019 ◽  
Vol 26 (15) ◽  
pp. 2558-2573 ◽  
Author(s):  
Murat Bozdag ◽  
Abdulmalik Saleh Alfawaz Altamimi ◽  
Daniela Vullo ◽  
Claudiu T. Supuran ◽  
Fabrizio Carta
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
State Of The Art ◽  
Cellular Level ◽  
Hydration Reaction ◽  
Carbonic Anhydrases ◽  
Crucial Importance ◽  
Learning Impairments ◽  
Current Review ◽  
Potential Use

The current review is intended to highlight recent advances in the search of new and effective modulators of the metalloenzymes Carbonic Anhydrases (CAs, EC 4.2.1.1) expressed in humans (h). CAs reversibly catalyze the CO2 hydration reaction, which is of crucial importance in the regulation of a plethora of fundamental processes at cellular level as well as in complex organisms. The first section of this review will be dedicated to compounds acting as activators of the hCAs (CAAs) and their promising effects on central nervous system affecting pathologies mainly characterized from memory and learning impairments. The second part will focus on the emerging chemical classes acting as hCA inhibitors (CAIs) and their potential use for the treatment of diseases.

scholarly journals EfficientPS: Efficient Panoptic Segmentation

2021 ◽  
Author(s):  
Rohit Mohan ◽  
Abhinav Valada
Keyword(s):  
State Of The Art ◽  
Autonomous Robot ◽  
Multi Scale ◽  
Contextual Features ◽  
New Variant ◽  
Competent Functioning ◽  
Segmentation Task

AbstractUnderstanding the scene in which an autonomous robot operates is critical for its competent functioning. Such scene comprehension necessitates recognizing instances of traffic participants along with general scene semantics which can be effectively addressed by the panoptic segmentation task. In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. We incorporate a new semantic head that aggregates fine and contextual features coherently and a new variant of Mask R-CNN as the instance head. We also propose a novel panoptic fusion module that congruously integrates the output logits from both the heads of our EfficientPS architecture to yield the final panoptic segmentation output. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark. Extensive evaluations on Cityscapes, KITTI, Mapillary Vistas and Indian Driving Dataset demonstrate that our proposed architecture consistently sets the new state-of-the-art on all these four benchmarks while being the most efficient and fast panoptic segmentation architecture to date.

scholarly journals Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

2019 ◽  
Vol 33 ◽  
pp. 865-872 ◽  
Author(s):  
Cheng Chen ◽  
Qi Dou ◽  
Hao Chen ◽  
Jing Qin ◽  
Pheng-Ann Heng
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
Target Domain ◽  
Learning Procedure ◽  
The Neural Networks ◽  
Feature Adaptation ◽  
Segmentation Task

This paper presents a novel unsupervised domain adaptation framework, called Synergistic Image and Feature Adaptation (SIFA), to effectively tackle the problem of domain shift. Domain adaptation has become an important and hot topic in recent studies on deep learning, aiming to recover performance degradation when applying the neural networks to new testing domains. Our proposed SIFA is an elegant learning diagram which presents synergistic fusion of adaptations from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features towards the segmentation task. The feature encoder layers are shared by both perspectives to grasp their mutual benefits during the end-to-end learning procedure. Without using any annotation from the target domain, the learning of our unified model is guided by adversarial losses, with multiple discriminators employed from various aspects. We have extensively validated our method with a challenging application of crossmodality medical image segmentation of cardiac structures. Experimental results demonstrate that our SIFA model recovers the degraded performance from 17.2% to 73.0%, and outperforms the state-of-the-art methods by a significant margin.

scholarly journals SOGNet: Scene Overlap Graph Network for Panoptic Segmentation

2020 ◽  
Vol 34 (07) ◽  
pp. 12637-12644 ◽  
Author(s):  
Yibo Yang ◽  
Hongyang Li ◽  
Xia Li ◽  
Qijie Zhao ◽  
Jianlong Wu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
The State ◽  
Graph Representation ◽  
Weak Supervision ◽  
Scene Graph ◽  
Art Performance ◽  
Segmentation Task ◽  
Instance Segmentation ◽  
Relation Matrix

The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene graph representation, we formulate the overlapping problem as a simplified case, named scene overlap graph. We leverage each object's category, geometry and appearance features to perform relational embedding, and output a relation matrix that encodes overlap relations. In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances. The mask logits after removing overlaps are fed into per-pixel instance id classification, which leverages the panoptic supervision to assist in the modeling of overlap relations. Besides, we generate an approximate ground truth of overlap relations as the weak supervision, to quantify the accuracy of overlap relations predicted by our method. Experiments on COCO and Cityscapes demonstrate that our method is able to accurately predict overlap relations, and outperform the state-of-the-art performance for panoptic segmentation. Our method also won the Innovation Award in COCO 2019 challenge.

scholarly journals Swarm Intelligence Integrated Graph-Cut for Liver Segmentation from 3D-CT Volumes

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Maya Eapen ◽  
Reeba Korah ◽  
G. Geetha
Keyword(s):  
Swarm Intelligence ◽  
Liver Volume ◽  
Graph Cut ◽  
Experimental Result ◽  
Liver Segmentation ◽  
Large Variability ◽  
Contrast Enhanced ◽  
Adaptive Weight ◽  
Efficiency And Effectiveness ◽  
Information Intensity

The segmentation of organs in CT volumes is a prerequisite for diagnosis and treatment planning. In this paper, we focus on liver segmentation from contrast-enhanced abdominal CT volumes, a challenging task due to intensity overlapping, blurred edges, large variability in liver shape, and complex background with cluttered features. The algorithm integrates multidiscriminative cues (i.e., prior domain information, intensity model, and regional characteristics of liver in a graph-cut image segmentation framework). The paper proposes a swarm intelligence inspired edge-adaptive weight function for regulating the energy minimization of the traditional graph-cut model. The model is validated both qualitatively (by clinicians and radiologists) and quantitatively on publically available computed tomography (CT) datasets (MICCAI 2007 liver segmentation challenge, 3D-IRCAD). Quantitative evaluation of segmentation results is performed using liver volume calculations and a mean score of 80.8% and 82.5% on MICCAI and IRCAD dataset, respectively, is obtained. The experimental result illustrates the efficiency and effectiveness of the proposed method.

scholarly journals PlaneNet: an efficient local feature extraction network

2021 ◽  
Vol 7 ◽  
pp. e783
Author(s):  
Bin Lin ◽  
Houcheng Su ◽  
Danyang Li ◽  
Ao Feng ◽  
Hongxiang Li ◽  
...  
Keyword(s):  
Mobile Devices ◽  
State Of The Art ◽  
Computing Time ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Light Weight ◽  
Practical Application ◽  
Classification Tasks ◽  
Segmentation Task ◽  
Local Feature Extraction

Due to memory and computing resources limitations, deploying convolutional neural networks on embedded and mobile devices is challenging. However, the redundant use of the 1 × 1 convolution in traditional light-weight networks, such as MobileNetV1, has increased the computing time. By utilizing the 1 × 1 convolution that plays a vital role in extracting local features more effectively, a new lightweight network, named PlaneNet, is introduced. PlaneNet can improve the accuracy and reduce the numbers of parameters and multiply-accumulate operations (Madds). Our model is evaluated on classification and semantic segmentation tasks. In the classification tasks, the CIFAR-10, Caltech-101, and ImageNet2012 datasets are used. In the semantic segmentation task, PlaneNet is tested on the VOC2012 datasets. The experimental results demonstrate that PlaneNet (74.48%) can obtain higher accuracy than MobileNetV3-Large (73.99%) and GhostNet (72.87%) and achieves state-of-the-art performance with fewer network parameters in both tasks. In addition, compared with the existing models, it has reached the practical application level on mobile devices. The code of PlaneNet on GitHub: https://github.com/LinB203/planenet.

scholarly journals SSCA-Net: Simultaneous Self- and Channel-Attention Neural Network for Multiscale Structure-Preserving Vessel Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiajia Ni ◽  
Jianhuang Wu ◽  
Jing Tong ◽  
Mingqiang Wei ◽  
Zhengming Chen
Keyword(s):  
Neural Network ◽  
High Performance ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Vessel Segmentation ◽  
Spatial Structures ◽  
Position Information ◽  
Structure Preserving ◽  
Intracranial Vessel ◽  
Multiscale Structure

Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to the complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multiscale structure-preserving vessel segmentation (MSVS) problem. SSCA-Net differs from the conventional neural networks in modeling image global contexts, showing more power to understand the global semantic information by both self- and channel-attention (SCA) mechanism and offering high performance on segmenting vessels with multiscale structures (e.g., DSC: 96.21% and MIoU: 92.70% on the intracranial vessel dataset). Specifically, the SCA module is designed and embedded in the feature decoding stage to learn SCA features at different layers, in which the self-attention is used to obtain the position information of the feature itself, and the channel attention is designed to guide the shallow features to obtain global feature information. To evaluate the effectiveness of our SSCA-Net, we compare it with several state-of-the-art methods on three well-known vessel segmentation benchmark datasets. Qualitative and quantitative results demonstrate clear improvements of our method over the state-of-the-art in terms of preserving vessel details and global spatial structures.

scholarly journals Document-level Relation Extraction as Semantic Segmentation

2021 ◽  
Author(s):  
Ningyu Zhang ◽  
Xiang Chen ◽  
Xin Xie ◽  
Shumin Deng ◽  
Chuanqi Tan ◽  
...  
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Segmentation ◽  
Experimental Results ◽  
Context Information ◽  
Global Information ◽  
Benchmark Datasets ◽  
Segmentation Task ◽  
Document Level

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.

scholarly journals An Adversarial and Densely Dilated Network for Connectomes Segmentation

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 467 ◽  
Author(s):  
Ke Chen ◽  
Dandan Zhu ◽  
Jianwei Lu ◽  
Ye Luo
Keyword(s):  
State Of The Art ◽  
Piriform Cortex ◽  
Receptive Fields ◽  
Ground Truth ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Boundary Map ◽  
Segmentation Task ◽  
The Brain

Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset.

Simulation of Manufacturing Processes via Virtual Reality

2018 ◽  
pp. 918-953
Author(s):  
Mohamed-Amine Abidi ◽  
Barbara Lyonnet ◽  
Pierre Chevaillier ◽  
Rosario Toscano ◽  
Patrick Baert
Keyword(s):  
Virtual Reality ◽  
Manufacturing System ◽  
State Of The Art ◽  
Simulation Method ◽  
Design Stage ◽  
Manufacturing Processes ◽  
Crucial Importance ◽  
Industrial Management ◽  
Technological Advances ◽  
Market Trends

In a world in continuous evolution, the different industrial actors need to be reactive to remain competitive and to conquer new market trends. To achieve this, they are constrained to improve their way of industrial management, both at the strategic level, to adapt to technological advances and follow market trends. In this chapter, we introduce a new simulation method that makes it easy to understand the results of a given simulation. This is of crucial importance because the design stage of a manufacturing system usually implies not specialist actors. The objective of the chapter is to present the main advantages of using the virtual reality (VR) to the manufacturing processes simulation. To this end, a state of the art will compose the first part of the chapter. In the second part, we address the issue of the contribution of the VR to the industrial simulation.

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