AIMC Topic: Imaging, Three-Dimensional

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A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image.

IEEE transactions on pattern analysis and machine intelligence
Three-dimensional shape reconstruction of 2D landmark points on a single image is a hallmark of human vision, but is a task that has been proven difficult for computer vision algorithms. We define a feed-forward deep neural network algorithm that can...

Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants.

Proceedings of the National Academy of Sciences of the United States of America
Some microbes possess the ability to adaptively manipulate host behavior. To better understand how such microbial parasites control animal behavior, we examine the cell-level interactions between the species-specific fungal parasite and its carpente...

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

Medical physics
PURPOSE: Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high ...

A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time.

Computers in biology and medicine
This work presents a data-driven method to simulate, in real-time, the biomechanical behavior of the breast tissues in some image-guided interventions such as biopsies or radiotherapy dose delivery as well as to speed up multimodal registration algor...

Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database.

IEEE transactions on medical imaging
The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT ...

Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation.

IEEE transactions on medical imaging
Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acqu...

Impact of pixel-based machine-learning techniques on automated frameworks for delineation of gross tumor volume regions for stereotactic body radiation therapy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
The aim of this study was to investigate the impact of pixel-based machine learning (ML) techniques, i.e., fuzzy-c-means clustering method (FCM), and the artificial neural network (ANN) and support vector machine (SVM), on an automated framework for ...

Tracking-by-detection of surgical instruments in minimally invasive surgery via the convolutional neural network deep learning-based method.

Computer assisted surgery (Abingdon, England)
BACKGROUND: Worldwide propagation of minimally invasive surgeries (MIS) is hindered by their drawback of indirect observation and manipulation, while monitoring of surgical instruments moving in the operated body required by surgeons is a challenging...

A combined learning algorithm for prostate segmentation on 3D CT images.

Medical physics
PURPOSE: Segmentation of the prostate on CT images has many applications in the diagnosis and treatment of prostate cancer. Because of the low soft-tissue contrast on CT images, prostate segmentation is a challenging task. A learning-based segmentati...