V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

Savioli, Nicoló; Montana, Giovanni; Lamata, Pablo

doi:10.48550/arxiv.1808.01944

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different works have considered the potential improvements of removing said volume partitioning [16,13]. Such fully-volumetric approach has already been applied to prostate [17], heart atrium [18], and proximal femur MRI segmentation [19], but not yet in the context of brain MRI segmentationwhere it could prove particularly useful given the complex geometry and the variety of structures characterising the brain anatomy. Here, we discuss how both hardware limitations and the scarcity of hand-labelled ground truth data can be overcome.…”

Section: Introductionmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

View full text Add to dashboard Cite

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.

show abstract

Section: Introductionmentioning

confidence: 99%

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi

Benini

Signoroni

et al. 2020

Medical Image Analysis

View full text Add to dashboard Cite

show abstract

“…In our previous research, we further propose a more effective standards Criteria, and realized automatic recognition and classification to DOBI images [8]. A practical segmentation model is learned by customizing a neural network architecture for a certain task or dataset and training it from scratch [9][10][11].…”

Section: Anji Reddy Vaka Et Al Proposed a New Method-deepmentioning

confidence: 99%

Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images

Chen¹,

Li²

2022

IJBSE

View full text Add to dashboard Cite

Breast cancer is the most common cancer in women. At present, the methods of examining lesions are generally mammography, or B-scanning and other methods with certain radioactive sources, which may lead to aggravation of breast lesions in young women. The dynamic optical breast scanning method uses infrared light to avoid the harm of X-rays to the human body. According to current research, doctors can use Dynamic Optical Breast Image (DOBI) to determine whether a patient has breast cancer. Studies have shown that convolutional neural networks (CNN) have higher detection accuracy in determining whether a patient has breast cancer. In this paper, we use an artificial intelligence neural network approach to analyze and process dynamic optical breast lesion images: we model the clinical lesion breast images in 3D, and use the VoxelMorph algorithm to segment the 3D images into 2D images; The time, space, location, and pathological trend curves in the image are analyzed and processed. We compared classification, sensitivity, and specific characteristics with the original dynamic breast lesion image scoring analysis system. The experimental results show that the accuracy is improved. At the same time, the problem of the original system's signature in the ROI area and leaf curve is solved. The use of CNN improves the analysis and processing speed, shortens the processing time, and increases the accuracy of the diagnostic reference from 83% to 90%.

show abstract

“…Fully convolutional neural networks (CNNs) like U-Net [15] have been the dominant approach in automatic medical imaging segmentation [4,11]. A practical segmentation model is learned by customizing a neural network architecture for a certain task or dataset and training it from scratch [11,16,18]. [7] learned a single segmentation CNN for brain datasets acquired with different scanners and/or protocols.…”

Section: Introductionmentioning

confidence: 99%

3D U$$^2$$-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation

Chao

Han

Yao

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmentation task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising universal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U 2 -Net) is built upon separable convolution, assuming that images from different domains have domain-specific spatial correlations which can be probed with channelwise convolution while also share cross-channel correlations which can be modeled with pointwise convolution. We evaluate the 3D U 2 -Net on five organ segmentation datasets. Experimental results show that this universal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about 1% of the parameters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U 2 -Net into public domain. 4

show abstract

V-FCNN: Volumetric Fully Convolution Neural Network For Automatic Atrial Segmentation

Cited by 3 publications

References 0 publications

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Based on Artificial Intelligence Neural Network CNN Method Analysis and Processing of Dynamic Optical Breast Lesion Images

3D U$$^2$$-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation

Contact Info

Product

Resources

About