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We propose a novel multi-stage trans-dimensional architecture for multi-view cardiac image segmentation. Our method exploits the relationship between long-axis (2D) and short-axis (3D) magnetic resonance (MR) images to perform a sequential…
Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postoperative assessment. However, the unusual conditions of…
Echocardiography plays an important part in diagnostic aid in cardiac diseases. A critical step in echocardiography-aided diagnosis is to extract the standard planes since they tend to provide promising views to present different structures…
Convolutional neural networks (CNN) for multi-class segmentation of medical images are widely used today. Especially models with multiple outputs that can separately predict segmentation classes (regions) without relying on a probabilistic…
Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning.…
Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing cardiac function. Precise segmentation of cardiac structures is imperative for accurate cardiac functional evaluation. This paper introduces a semi-supervised model…
For extending CT field-of-view to perform non-destructive testing, the Symmetric Multi-Linear trajectory Computed Tomography (SMLCT) has been developed as a successful example of non-standard CT scanning modes. However, inevitable geometric…
The human lung is a complex respiratory organ, consisting of five distinct anatomic compartments called lobes. Accurate and automatic segmentation of these pulmonary lobes from computed tomography (CT) images is of clinical importance for…
As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. Following the clinical guidelines, estimation of…
The objective of this study was to develop a PET tumor-segmentation framework that addresses the challenges of limited spatial resolution, high image noise, and lack of clinical training data with ground-truth tumor boundaries in PET…
Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Several convolutional neural network (CNN) architectures have been proposed to segment the…
Background: Changes in choroidal thickness are associated with various ocular diseases and the choroid can be imaged using spectral-domain optical coherence tomography (SDOCT) and enhanced depth imaging OCT (EDIOCT). New Method: Eighty…
Semantic segmentation is a crucial task in medical image processing, essential for segmenting organs or lesions such as tumors. In this study we aim to improve automated segmentation in CBCTs through multi-task learning. To evaluate effects…
The purpose of this study is to develop an automated algorithm for thoracic vertebral segmentation on chest radiography using deep learning. 124 de-identified lateral chest radiographs on unique patients were obtained. Segmentations of…
Cardiovascular diseases are among the leading causes of death globally. Cardiac left ventricle (LV) quantification is known to be one of the most important tasks for the identification and diagnosis of such pathologies. In this paper, we…
Semantic segmentation is an important task in computer vision that is often tackled with convolutional neural networks (CNNs). A CNN learns to produce pixel-level predictions through training on pairs of images and their corresponding…
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures. In this work, a…
A major obstacle to building models for effective semantic segmentation, and particularly video semantic segmentation, is a lack of large and well annotated datasets. This bottleneck is particularly prohibitive in highly specialized and…
Background: A crucial initial processing step for quantitative PET/CT analysis is the segmentation of tumor lesions enabling accurate feature ex-traction, tumor characterization, oncologic staging, and image-based therapy response…
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality…