Introduction: Nowadays virtual colonoscopy has become a reliable and efficient method of detecting primary stages of colon cancer such as polyp detection. One of the most important and crucial stages of virtual colonoscopy is colon segmentation because an incorrect segmentation may lead to a misdiagnosis.  Materials and Methods: In this work, a hybrid method based on Geometric Deformable Models...

Deep co-training has recently been proposed as an effective approach for image segmentation when annotated data is scarce. In this paper, we improve existing approaches semi-supervised with a self-paced and self-consistent method. To help distillate information from unlabeled images, first design learning strategy that lets jointly-trained neural networks focus on easier-to-segment regions firs...

segmentation and three‑dimensional (3d) visualization of teeth in dental computerized tomography (ct) images are of dentists’ requirements for both abnormalities diagnosis and the treatments such as dental implant and orthodontic planning. on the other hand, dental ct image segmentation is a difficult process because of the specific characteristics of the tooth’s structure. this paper presents ...

like other sciences, market segmentation, as a science, seeks to realize unique needs of human being. in health market, individual diagnostic and curative methods are practiced more than anytime. however, a foundation of this market, pharmaceutical market, is not as advanced as other products and services markets in terms of segmentation. regarding the complexity of pharmaceutical market, it is...

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress multi-view stereo (MVS). However, existing rely the assumption corresponding points among different views share same color, which may not always be true in practice. This lead to unreliable signal and harm final reconstruction performance. To address issue, we propose a framework integrated ...

Introduction:  The  advent  of  dual-modality  PET/CT  scanners  has  revolutionized  clinical  oncology  by  improving lesion localization and facilitating treatment planning for radiotherapy. In addition, the use of  CT images for CT-based attenuation correction (CTAC) decreases the overall scanning time and creates  a noise-free  attenuation  map  (6map).  CTAC  methods  include  scaling,  s...

Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on handcrafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then ...

introduction: various computer assisted medical procedures such as dental implant, orthodontic planning, face, jaw and cosmetic surgeries require automatic quantification and volumetric visualization of teeth. in this regard, segmentation is a major step. material and methods: in this paper, inspired by our previous experiences and considering the anatomical knowledge of teeth and jaws, we prop...

background and objectives: identification of surgical instruments in laparoscopic video images has several biomedical applications. while several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. this paper introduces a surgical instrument detection framework (sidf) for a...

breast lesion segmentation in mr images is one of the most important parts of clinical diagnostic tools. pixel classification methods have been frequently used in image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy, but they need a large amount of labeled data, which is hard, expensive, and slow to be obtained. on t...