CSSNet: Cascaded spatial shift network for multi-organ segmentation.

Multi-organ segmentation is vital for clinical diagnosis and treatment. Although CNN and its extensions are popular in organ segmentation, they suffer from the local receptive field. In contrast, MultiLayer-Perceptron-based models (e.

FAFuse: A Four-Axis Fusion framework of CNN and Transformer for medical image segmentation.

Medical image segmentation is crucial for accurate diagnosis and treatment in the medical field. In recent years, convolutional neural networks (CNNs) and Transformers have been frequently adopted as network architectures in medical image segmentation. The convolution operation is limited in modeling long-range dependencies because it can only extract local information through the limited receptive field.

DIBR-Synthesized Image Quality Assessment With Texture and Depth Information.

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images.

Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT From 3D Bounding Box Annotations.

Obtaining accurate segmentation of the prostate and nearby organs at risk (e.g., bladder and rectum) in CT images is critical for radiotherapy of prostate cancer.

CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation.

Sufficient data with complete annotation is essential for training deep models to perform automatic and accurate segmentation of CT male pelvic organs, especially when such data is with great challenges such as low contrast and large shape variation. However, manual annotation is expensive in terms of both finance and human effort, which usually results in insufficient completely annotated data in real applications. To this end, we propose a novel deep framework to segment male pelvic organs in CT images with incomplete annotation delineated in a very user-friendly manner.

Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model.

Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements.

Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images.

Purpose: Accurate 3D image segmentation is a crucial step in radiation therapy planning of head and neck tumors. These segmentation results are currently obtained by manual outlining of tissues, which is a tedious and time-consuming procedure. Automatic segmentation provides an alternative solution, which, however, is often difficult for small tissues (i.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.

Lung field segmentation using weighted sparse shape composition with robust initialization.

Purpose: Lung field segmentation for chest radiography is critical to pulmonary disease diagnosis. In this paper, we propose a new deformable model using weighted sparse shape composition with robust initialization to achieve robust and accurate lung field segmentation.

Methods: Our method consists of three steps: initialization, deformation and regularization.

Prostate deformation from inflatable rectal probe cover and dosimetric effects in prostate seed implant brachytherapy.

Purpose: Prostate brachytherapy is an important treatment technique for patients with localized prostate cancer. An inflatable rectal ultrasound probe cover is frequently utilized during the procedure to adjust for unfavorable prostate position relative to the implant grid. However, the inflated cover causes prostate deformation, which is not accounted for during dosimetric planning.

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs.

Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images.

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector.

Hierarchical lung field segmentation with joint shape and appearance sparse learning.

Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation.

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.

Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images.

Segmenting the prostate from CT images is a critical step in the radio-therapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians.