Catheter detection in 3D ultrasound by CNN

Hongxu Yang; Caifeng Shan; Alexander F. Kolen; Peter de With

Catheter detection in 3D ultrasound by CNN

Hongxu Yang
, Caifeng Shan
, Alexander F. Kolen
, Peter de With

Research output: Contribution to conference › Other › Academic

2 Downloads (Pure)

Abstract

In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanarbased CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected
in noisy and low-resolution images.

Original language	English
Number of pages	2
Publication status	Published - 2018
Event	The Netherlands Conference on Computer Vision (NCCV 2018) - Duration: 26 Sept 2018 → 27 Sept 2018

Conference

Conference	The Netherlands Conference on Computer Vision (NCCV 2018)
Abbreviated title	NCCV 2018
Period	26/09/18 → 27/09/18

Cite this

@conference{239736ceb4d84905b3663ae1b8e9db86,

title = "Catheter detection in 3D ultrasound by CNN",

abstract = "In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanarbased CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images.",

author = "Hongxu Yang and Caifeng Shan and Kolen, \{Alexander F.\} and \{de With\}, Peter",

year = "2018",

language = "English",

note = "The Netherlands Conference on Computer Vision (NCCV 2018), NCCV 2018 ; Conference date: 26-09-2018 Through 27-09-2018",

}

TY - CONF

T1 - Catheter detection in 3D ultrasound by CNN

AU - Yang, Hongxu

AU - Shan, Caifeng

AU - Kolen, Alexander F.

AU - de With, Peter

PY - 2018

Y1 - 2018

N2 - In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanarbased CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images.

AB - In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanarbased CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images.

M3 - Other

T2 - The Netherlands Conference on Computer Vision (NCCV 2018)

Y2 - 26 September 2018 through 27 September 2018

ER -

Catheter detection in 3D ultrasound by CNN

Abstract

Conference

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Cite this