Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
Going deep in medical image analysis: concepts, methods, challenges, and future directions
Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …
technology has recently attracted so much interest of the Medical Imaging Community that it …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
[HTML][HTML] Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem
J Hofmanninger, F Prayer, J Pan, S Röhrich… - European Radiology …, 2020 - Springer
Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
analysis. For lung segmentation in computed tomography, a variety of approaches exists …
Learning for disparity estimation through feature constancy
Stereo matching algorithms usually consist of four steps, including matching cost calculation,
matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN …
matching cost aggregation, disparity calculation, and disparity refinement. Existing CNN …
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation
We aim at segmenting small organs (eg, the pancreas) from abdominal CT scans. As the
target often occupies a relatively small region in the input image, deep neural networks can …
target often occupies a relatively small region in the input image, deep neural networks can …
CT-realistic lung nodule simulation from 3D conditional generative adversarial networks for robust lung segmentation
Data availability plays a critical role for the performance of deep learning systems. This
challenge is especially acute within the medical image domain, particularly when …
challenge is especially acute within the medical image domain, particularly when …
Semi-supervised 3D abdominal multi-organ segmentation via deep multi-planar co-training
In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep
learning algorithms require lots of voxel-wise annotations, which are usually difficult …
learning algorithms require lots of voxel-wise annotations, which are usually difficult …
[HTML][HTML] Automated quantification of COVID-19 severity and progression using chest CT images
J Pu, JK Leader, A Bandos, S Ke, J Wang, J Shi… - European …, 2021 - Springer
Objective To develop and test computer software to detect, quantify, and monitor
progression of pneumonia associated with COVID-19 using chest CT scans. Methods One …
progression of pneumonia associated with COVID-19 using chest CT scans. Methods One …