Creation of a female and male segmentation dataset based on Chinese Visible Human (CVH)

Wu, Yi; Tan, Liwen; Li, Ying; Fang, Bin-Ji; Xie, Bing; Wu, Tong; Li, Qiyu; Qiu, Mingguo; Liu, Guang-Jiu; Li, Kai; Xu, Haotong; Luo, Na; Zhang, Shaoxiang

doi:10.1016/j.compmedimag.2012.01.003

Cited by 20 publications

(11 citation statements)

References 24 publications

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“…In contrast, the thin-slice cross-sectional images deprived from Visible Human data sets are preferable to the traditional two-dimensional images due to the high resolution, thinner image thickness, and presence of actual anatomical color. The images based on these data sets have an advantage in identifying and outlining the subtle structures without any deformation and loss, such as muscles, fascia, and ligaments [20–23]. …”

Section: Introductionmentioning

confidence: 99%

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

Chen

Tao

et al. 2017

Med Sci Monit

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BackgroundThe aim of this study was to use a three-dimensional (3D) visualization technology to illustrate and describe the anatomical features of the penile suspensory ligamentous system based on the Visible Human data sets and to explore the suspensory mechanism of the penis for the further improvement of the penis-lengthening surgery.Material/MethodsCross-sectional images retrieved from the first Chinese Visible Human (CVH-1), third Chinese Visible Human (CVH-3), and Visible Human Male (VHM) data sets were used to segment the suspensory ligamentous system and its adjacent structures. The magnetic resonance imaging (MRI) images of this system were studied and compared with those from the Visible Human data sets. The 3D models reconstructed from the Visible Human data sets were used to provide morphological features of the penile suspensory ligamentous system and its related structures.ResultsThe fundiform ligament was a superficial, loose, fibro-fatty tissue which originated from Scarpa’s fascia superiorly and continued to the scrotal septum inferiorly. The suspensory ligament and arcuate pubic ligament were dense fibrous connective tissues which started from the pubic symphysis and terminated by attaching to the tunica albuginea of the corpora cavernosa. Furthermore, the arcuate pubic ligament attached to the inferior rami of the pubis laterally.ConclusionsThe 3D model based on Visible Human data sets can be used to clarify the anatomical features of the suspensory ligamentous system, thereby contributing to the improvement of penis-lengthening surgery.

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Section: Introductionmentioning

confidence: 99%

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

Chen

Tao

et al. 2017

Med Sci Monit

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“…This popular software has been preferred by the Visible Korean laboratory [12], the Chinese Visible team [15] and others [2,14]. The Visible Korean team has carried out the next procedure, surface reconstruction, using assorted software, most recently 3D-DOCTOR version 4 (Able Software, Lexington, MA, USA) [6,7,11,13].…”

Section: Discussionmentioning

confidence: 99%

Segmentation and surface reconstruction of a cadaver heart on Mimics software

et al. 2015

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“…The anatomical structures of the brain tissues are very complex and associated with a number of neurological diseases. Nevertheless, without segmentation, the computer cannot recognize and define a tissue's contour automatically, and the anatomical images are difficult to be used for lateral medical application [ 1 ]. Cryosection images in the Chinese Visible Human (CVH) dataset show the true color of the human body in a high spatial resolution and contain more rich and original details of the brain anatomy than other medical imaging, such as CT and MRI [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

Zhao

Wang

Niu

et al. 2016

BioMed Research International

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Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

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Creation of a female and male segmentation dataset based on Chinese Visible Human (CVH)

Cited by 20 publications

References 24 publications

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

Segmentation and surface reconstruction of a cadaver heart on Mimics software

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

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