Finding abnormal lymph nodes in radiological images is highly important ...
Estimating displacement vector field via a cost volume computed in the
f...
Radiotherapists require accurate registration of MR/CT images to effecti...
Intrathoracic airway segmentation in computed tomography (CT) is a
prere...
Open international challenges are becoming the de facto standard for
ass...
Deep learning empowers the mainstream medical image segmentation methods...
Background: The current clinical workflow for esophageal gross tumor vol...
In this work, we introduce a fast and accurate method for unsupervised 3...
Lymph node station (LNS) delineation from computed tomography (CT) scans...
Radiological images such as computed tomography (CT) and X-rays render
a...
Large-scale datasets with high-quality labels are desired for training
a...
Determining the spread of GTV_LN is essential in defining the respective...
Finding, identifying and segmenting suspicious cancer metastasized lymph...
Accurate and automated tumor segmentation is highly desired since it has...
Lesion detection is an important problem within medical imaging analysis...
Finding and identifying scatteredly-distributed, small, and critically
i...
OAR segmentation is a critical step in radiotherapy of head and neck (H ...
Hip and pelvic fractures are serious injuries with life-threatening
comp...
Clinical target volume (CTV) delineation from radiotherapy computed
tomo...
Gross tumor volume (GTV) segmentation is a critical step in esophageal c...
Quantification of cerebral white matter hyperintensities (WMH) of presum...
Data availability plays a critical role for the performance of deep lear...
Segmentation and quantification of white matter hyperintensities (WMHs) ...