Transformer-based arterial spin labeling perfusion MRI denoising.

Arterial Spin Labeling (ASL) perfusion MRI is the only non-invasive technique for quantifying regional cerebral blood flow (CBF) visualization, which is an important physiological variable. ASL MRI has a relatively low signal-to-noise-ratio (SNR), making it challenging to achieve high quality CBF images using limited data. Promising ASL CBF denoising results have been shown in recent convolutional neural network (CNN)-based methods.

ECSU-Net: An Embedded Clustering Sliced U-Net Coupled With Fusing Strategy for Efficient Intervertebral Disc Segmentation and Classification.

Automatic vertebra segmentation from computed tomography (CT) image is the very first and a decisive stage in vertebra analysis for computer-based spinal diagnosis and therapy support system. However, automatic segmentation of vertebra remains challenging due to several reasons, including anatomic complexity of spine, unclear boundaries of the vertebrae associated with spongy and soft bones. Based on 2D U-Net, we have proposed an Embedded Clustering Sliced U-Net (ECSU-Net).

Living Donor-Recipient Pair Matching for Liver Transplant via Ternary Tree Representation With Cascade Incremental Learning.

Visual understanding of liver vessels anatomy between the living donor-recipient (LDR) pair can assist surgeons to optimize transplant planning by avoiding non-targeted arteries which can cause severe complications. We propose to visually analyze the anatomical variants of the liver vessels anatomy to maximize similarity for finding a suitable Living Donor-Recipient (LDR) pair. Liver vessels are segmented from computed tomography angiography (CTA) volumes by employing a cascade incremental learning (CIL) model.

SPST-CNN: Spatial pyramid based searching and tagging of liver's intraoperative live views via CNN for minimal invasive surgery.

Laparoscopic liver surgery is challenging to perform because of compromised ability of the surgeon to localize subsurface anatomy due to minimal invasive visibility. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflations and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The navigation ability in terms of searching and tagging within liver views has not been characterized, and current object detection methods do not account for the mechanics of how these features could be applied to the liver images.

Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images.

An efficient and precise liver extraction from computed tomography (CT) images is a crucial step for computer-aided hepatic diseases diagnosis and treatment. Considering the possible risk to patient's health due to X-ray radiation of repetitive CT examination, low-dose CT (LDCT) is an effective solution for medical imaging. However, inhomogeneous appearances and indistinct boundaries due to additional noise and streaks artifacts in LDCT images often make it a challenging task.

Image-Aligned Dynamic Liver Reconstruction Using Intra-Operative Field of Views for Minimal Invasive Surgery.

During hepatic minimal invasive surgery (MIS), 3-D reconstruction of a liver surface by interpreting the geometry of its soft tissues is achieving attractions. One of the major issues to be addressed in MIS is liver deformation. Moreover, it severely inhibits free sight and dexterity of tissue manipulation, which causes its intra-operative morphology and soft tissue motion altered as compared to its pre-operative shape.