Deep learning methods achieved remarkable results in medical image analysis tasks but it has not yet been widely used by medical professionals. One of the main reasons for this restricted usage is the uncertainty of the reasons that influence the decision of the model. Explainable AI methods have been developed to improve the transparency, interpretability, and explainability of the black-box AI methods.
View Article and Find Full Text PDFPurpose: Random forests and dictionary-based statistical regressions have common characteristics, including non-linear mapping and supervised learning. To reduce the reconstruction error of high-resolution images, we integrate random forests and coupled dictionary learning.
Methods: Textural differences of image blocks are considered by the classification of patches using an Auto-Encoder network.
Diagnosis techniques based on medical image modalities have higher sensitivities compared to conventional RT-PCT tests. We propose two methods for diagnosing COVID-19 disease using X-ray images and differentiating it from viral pneumonia. The diagnosis section is based on deep neural networks, and the discriminating uses an image retrieval approach.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
February 2020
Purpose: Convolutional neural networks (CNNs) have obtained enormous success in liver segmentation. However, there are several challenges, including low-contrast images, and large variations in the shape, and appearance of the liver. Incorporating prior knowledge in deep CNN models improves their performance and generalization.
View Article and Find Full Text PDFExtraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data. We propose an automatic and robust vessel segmentation approach that uses a multi-pathways deep learning network.
View Article and Find Full Text PDFPurpose: The accurate delineation of hepatic vessels is important to diagnosis and treatment planning. To improve the segmentation of these vessels and extract small structures, we adaptively calculate the data term in conventional graph-cuts algorithm.
Method: To assign higher costs to the data term in small vessel regions, we estimate the statistical parameters of the vessel adaptively.
Purpose: To improve segmentation of normal/abnormal livers in contrast-enhanced/non-contrast CT image using the Active Shape Model (ASM) algorithm; we introduce a generalized profile model. We also intend to accurately detect boundary of liver where it touches nearby organs with similar intensities.
Method: Initial boundary of a liver in a CT slice is found using an intensity-based technique and it is then represented by a set of points.
Int J Comput Assist Radiol Surg
July 2016
Purpose: The intensity profile of an image in the vicinity of a tissue's boundary is modeled by a step/ramp function. However, this assumption does not hold in cases of low-contrast images, heterogeneous tissue textures, and where partial volume effect exists. We propose a hybrid algorithm for segmentation of CT/MR tumors in low-contrast, noisy images having heterogeneous/homogeneous or hyper-/hypo-intense abnormalities.
View Article and Find Full Text PDFAccurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
August 2015
Purpose: Multimodality registration of liver CT and MRI scans is challenging due to large initial misalignment, non-uniform MR signal intensity in the liver parenchyma, incomplete liver shapes in Open-MR scans and non-rigid deformations of the organ. An automated method was developed to register liver CT and open-MRI scans.
Methods: A hybrid registration algorithm was developed which incorporates both rigid and non-rigid methods.
The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
November 2014
Purpose: Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
August 2015
In this paper, we presented our newly developed computer-aided liver surgical planning system for patient-specific treatments by using the patient's CT volumes. The system is composed of three modules, liver segmentation, vessel extraction, and visualization & interaction modules. It can prepare a virtual environment for patient-specific liver surgical planning and simulations.
View Article and Find Full Text PDFTo compensate for bias field inhomogeneity and reduce noise, we incorporate domain-based knowledge and spatial information into a brain segmentation algorithm by proposing a new multi-layer Hidden Markov model. Brain tissues include Gray Matter (GM), White Matter (WM), and Cerebrospinal Fluid (CSF). A typical slice of a brain image either contains GM, GM-WM or GM-WM-CSF.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
March 2012
Purpose: Extraction and enhancement of tubular structures are important in image processing applications, especially in the analysis of liver CT scans where delineation of vascular structures is needed for surgical planning. Portal vein cross-sections have circular or elliptical shapes, so an algorithm must accommodate both. A vessel segmentation method based on medial-axis points was developed and tested on portal veins in CT images.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
May 2009
Comput Med Imaging Graph
December 2009
Liver cancer is one of the major death factors in the world. Transplantation and tumor removal are two main therapies in common clinical practice. Both tasks need image assisted planning and quantitative evaluations.
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