Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

Qadri, Syed Furqan; Ai, Danni; Hu, Guoyu; Ahmad, Mubashir; Huang, Yong; Wang, Yongtian; Yang, Jian

doi:10.3390/app9010069

Cited by 53 publications

(25 citation statements)

References 49 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, semantic segmentation is the process of labeling each pixel of an image with a predefined class. In recent years, researchers focusing towards deep learning-based image segmentation due to the availability of online resource materials, easy accessibility of high computational power, availability of computer vision and other supporting libraries, and the potential of Convolution Neural Network architecture in obtaining effective segmentation results [35][36][37][38][39]. In the literature, an interesting application involving neural networks and more broadly computer vision techniques is microscopy image analysis [40,41], the analogy with our domain stands in various environmental factors which can lead to a false interpretation of the results by human professionals.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

View full text Add to dashboard Cite

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…The smoothness of images is controlled by the use of a Gaussian filter with a fixed kernel size on the histopathology images. Gaussian filter plays an important role in achieving classification accuracy and reducing the weight of bluring pixels [71]. In this way, our model will able to learn to distinguish between a positive sample and a negative sample more accurately.…”

Section: A Preprocessingmentioning

confidence: 99%

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

et al. 2021

Self Cite

View full text Add to dashboard Cite

Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging. In this study, a novel patchbased deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN). Features are extracted through an unsupervised pre-training and supervised fine-tuning phase. The network automatically extracts features from image patches. Logistic regression is used to classify the patches from histopathology images. The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background). The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86%. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods.

show abstract

“…Qadri et al developed an automatic approach, named patch-based deep belief networks (PaDBNs), for vertebrae segmentation in CT images [11]. Deep belief networks (DBNs) are DL models composed of stacked Restricted Boltzmann Machines (RBMs) [14].…”

Section: Related Workmentioning

confidence: 99%

Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

et al. 2021

View full text Add to dashboard Cite

The accurate segmentation and identification of vertebrae presents the foundations for spine analysis including fractures, malfunctions and other visual insights. The large-scale vertebrae segmentation challenge (VerSe), organized as a competition at the Medical Image Computing and Computer Assisted Intervention (MICCAI), is aimed at vertebrae segmentation and labeling. In this paper, we propose a framework that addresses the tasks of vertebrae segmentation and identification by exploiting both deep learning and classical machine learning methodologies. The proposed solution comprises two phases: a binary fully automated segmentation of the whole spine, which exploits a 3D convolutional neural network, and a semi-automated procedure that allows locating vertebrae centroids using traditional machine learning algorithms. Unlike other approaches, the proposed method comes with the added advantage of no requirement for single vertebrae-level annotations to be trained. A dataset of 214 CT scans has been extracted from VerSe’20 challenge data, for training, validating and testing the proposed approach. In addition, to evaluate the robustness of the segmentation and labeling algorithms, 12 CT scans from subjects affected by severe, moderate and mild scoliosis have been collected from a local medical clinic. On the designated test set from Verse’20 data, the binary spine segmentation stage allowed to obtain a binary Dice coefficient of 89.17%, whilst the vertebrae identification one reached an average multi-class Dice coefficient of 90.09%. In order to ensure the reproducibility of the algorithms hereby developed, the code has been made publicly available.

show abstract

Automatic Deep Feature Learning via Patch-Based Deep Belief Network for Vertebrae Segmentation in CT Images

Cited by 53 publications

References 49 publications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Breast Cancer Classification From Histopathological Images Using Patch-Based Deep Learning Modeling

Segmentation and Identification of Vertebrae in CT Scans Using CNN, k-Means Clustering and k-NN

Contact Info

Product

Resources

About