[HTML][HTML] A review: Deep learning for medical image segmentation using multi-modality fusion
Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
A review of deep learning based methods for medical image multi-organ segmentation
Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …
performance in many medical image segmentation tasks. Many deep learning-based …
Advances in auto-segmentation
Manual image segmentation is a time-consuming task routinely performed in radiotherapy to
identify each patient's targets and anatomical structures. The efficacy and safety of the …
identify each patient's targets and anatomical structures. The efficacy and safety of the …
Survey on deep learning for radiotherapy
More than 50% of cancer patients are treated with radiotherapy, either exclusively or in
combination with other methods. The planning and delivery of radiotherapy treatment is a …
combination with other methods. The planning and delivery of radiotherapy treatment is a …
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
High-level prior-based loss functions for medical image segmentation: A survey
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …
performance for supervised medical image segmentation, across various imaging modalities …
Deep convolutional neural network for segmentation of thoracic organs‐at‐risk using cropped 3D images
Purpose Automatic segmentation of organs‐at‐risk (OAR s) is a key step in radiation
treatment planning to reduce human efforts and bias. Deep convolutional neural networks …
treatment planning to reduce human efforts and bias. Deep convolutional neural networks …
Cascaded SE-ResUnet for segmentation of thoracic organs at risk
Computed Tomography (CT) has been widely used in the planning of radiation therapy,
which is one of the most effective clinical lung cancer treatment options. Accurate …
which is one of the most effective clinical lung cancer treatment options. Accurate …
Automatic delineation of cardiac substructures using a region‐based fully convolutional network
Purpose Radiation dose to specific cardiac substructures, such as the atria and ventricles,
has been linked to post‐treatment toxicity and has shown to be more predictive of these …
has been linked to post‐treatment toxicity and has shown to be more predictive of these …
Deep learning in multi-organ segmentation
This paper presents a review of deep learning (DL) in multi-organ segmentation. We
summarized the latest DL-based methods for medical image segmentation and applications …
summarized the latest DL-based methods for medical image segmentation and applications …