A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network

Yang, Yangzi; Jiang, Huiyan; Sun, Qingjiao

doi:10.1155/2017/6941306

Cited by 10 publications

(9 citation statements)

References 24 publications

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“…In the network structure, there are only convolution layer and pooling layer. There is no full connection layer [12,13]. The difference of the two network structures is that up-sampling and down-sampling in U-Net network adopt the same level of convolution operation.…”

Section: U-net Networkmentioning

confidence: 99%

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

et al. 2019

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Aviation bearing assembled detection is the final barrier to quality and safety. Therefore, an accurate detection method of aviation bearing that is based on local characteristics is designed to solve the detection problem of mis-assembly and miss-assembly of balls in aviation bearing assembled. When considering the spatial limitation of aviation bearing assembled image acquisition, the dynamic distribution of balls and the interference of lubricating grease on the surface, a dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is designed to achieve the accurate segmentation of the local ball region of aviation bearing. Subsequently, an incomplete circle fitting algorithm is designed based on the segmented local ball image and Hough transform principle. These two algorithms make the measurement error of aviation bearing ball size less than 100 μm. Using bearings validates the algorithm. The results show that the accuracy of dynamic local ball segmentation model that is based on U-Net network with symmetrical structure is over 99%. At the same time, on the basis of accurate segmentation in aviation bearing local ball, the designed Hough circle algorithm is used for circle detection. The experimental results show that the false detection rate of mis-assembly and miss-assembly of balls is less than 3%. Further, the goal of zero-missed detection of mis-assembly and miss-assembly of balls in aviation bearing is achieved. The accurate segmentation of aviation bearing local ball and the effective identification of mis-assembly and miss-assembly of balls are realized. This method can provide a theory for the improvement of mis-assembly and miss-assembly of balls detection in aviation bearing. Furthermore, it has high application value.

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Section: U-net Networkmentioning

confidence: 99%

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

et al. 2019

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“…Quite a few studies reported multiorgan segmentation with duodenum organ which plays crucial role in video-endoscopy. Thus, satisfactory research work is required for clinical acceptance for small organs especially pancreas and duodenum [2].…”

Section: A Organ Selectionmentioning

confidence: 99%

“…But it is impractical solution to have radiologists for manual individual organ detection and it consumes more resources such as time, material and labor. That's why lots of work has been conducted to automate organ segmentation systems [2]. Although huge amount of research is carried out for single organ segmentation [3]- [8] but more attention is yet to be required for multiorgan segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…It is helpful in inserting endoscopy in endoscopic procedure [12]. In many clinical procedures such as detection of metastasis [13]- [14] and radiotherapy [2] requires analysis of different anatomies.…”

Section: Introductionmentioning

confidence: 99%

“…Better results can be achieved by combining different models. Yangzi Yang et al[2] built new architecture namely FCN-DecNet. It is based on FCN model and more layers are added to it such as de-convolutional layer, fusion layer and new unpooling layer.…”

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confidence: 99%

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Multiorgan Detection: Deep Learning Based Techniques and Research Directions

Kaur*,

Kaur,

Neeru

2019

ijitee

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Abstract: Automatic organ segmentation plays an important role in clinical procedures such as planning of radiation therapies and in computer-aided diagnostic systems. Several state-of -art techniques are available for multiorgan segmentation, however deep learning methods are doing exceptionally well and become the methodology of choice to analyze medical images. This intensively carried out work is conducted for deep learning methods applied on various organs in abdominal CT images. Firstly, this paper formulates segmentation, semantic segmentation problem and their methods. Secondly, multiorgan detection techniques based on deep learning along with their contributions, chosen datasets and gaps are discussed. It presents the metrics used to evaluate these methods. Finally, interesting conclusions has been drawn which will add to do future work using deep learning.

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Automatic segmentation of temporal bone structures from clinical conventional CT using a CNN approach

et al. 2021

Robotics Computer Surgery

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Background Automatic segmentation of temporal bone structures from patients' conventional computed tomography (CT) data plays an important role in the image‐guided cochlear implant surgery. Existing convolutional neural network approaches have difficulties in segmenting such small tubular structures. Methods We propose a light‐weight three‐dimensional convolutional neural network referred to as W‐Net to achieve multiobjective segmentation of temporal bone structures including the cochlear labyrinth, ossicular chain and facial nerve from conventional temporal bone CT images. Data augmentation with morphological enhancement is proposed to increase the segmentation accuracy of small tubular structures. Evaluation against the state‐of‐the‐art methods is performed. Results Our method achieved mean Dice similarity coefficients (DSCs) of 0.90, 0.85 and 0.77 for the cochlear labyrinth, ossicular chain and facial nerve, respectively. These results were also close to the DSCs between human expert annotators (0.91, 0.91 and 0.72). Conclusions Our method achieves human‐level accuracy in the segmentation of the cochlear labyrinth, ossicular chain and facial nerve.

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A Multiorgan Segmentation Model for CT Volumes via Full Convolution-Deconvolution Network

Cited by 10 publications

References 24 publications

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

Accurate Detection Method of Aviation Bearing Based on Local Characteristics

Multiorgan Detection: Deep Learning Based Techniques and Research Directions

Automatic segmentation of temporal bone structures from clinical conventional CT using a CNN approach

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