A bayesian approach for liver analysis: Algorithm and validation study
M Freiman, O Eliassaf, Y Taieb, L Joskowicz… - … Image Computing and …, 2008 - Springer
We present a new method for the simultaneous, nearly automatic segmentation of liver
contours, vessels, and metastatic lesions from abdominal CTA scans. The method …
contours, vessels, and metastatic lesions from abdominal CTA scans. The method …
A fast method for whole liver-and colorectal liver metastasis segmentations from MRI using 3d FCNN networks
The liver is the most frequent organ for metastasis from colorectal cancer, one of the most
common tumor types with a poor prognosis. Despite reducing surgical planning time and …
common tumor types with a poor prognosis. Despite reducing surgical planning time and …
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the
potential to substantially enhance tumor treatment strategies for HCC patients. In this work …
potential to substantially enhance tumor treatment strategies for HCC patients. In this work …
A novel automatic liver segmentation technique for MR images
This paper presents an automatic liver segmentation algorithm based on fast marching and
improved fuzzy cluster methods, which can segment liver from abdominal MR images …
improved fuzzy cluster methods, which can segment liver from abdominal MR images …
Ensemble learning based segmentation of metastatic liver tumours in contrast-enhanced computed tomography
A Shimizu, T Narihira, H Kobatake… - … on Information and …, 2013 - search.ieice.org
This paper presents an ensemble learning algorithm for liver tumour segmentation from a
CT volume in the form of U-Boost and extends the loss functions to improve performance …
CT volume in the form of U-Boost and extends the loss functions to improve performance …
A deep learning-based interactive medical image segmentation framework with sequential memory
I Mikhailov, B Chauveau, N Bourdel, A Bartoli - Computer Methods and …, 2024 - Elsevier
Background and objective. Image segmentation is an essential component in medical image
analysis. The case of 3D images such as MRI is particularly challenging and time …
analysis. The case of 3D images such as MRI is particularly challenging and time …
Liver segmentation from computed tomography images using cascade deep learning
JDL Araújo, LB da Cruz, JOB Diniz, JL Ferreira… - Computers in biology …, 2022 - Elsevier
Background Liver segmentation is a fundamental step in the treatment planning and
diagnosis of liver cancer. However, manual segmentation of liver is time-consuming …
diagnosis of liver cancer. However, manual segmentation of liver is time-consuming …
Deep learning based automatic liver tumor segmentation in CT with shape-based post-processing
Accurate automatic liver tumor segmentation would have a big impact on liver therapy
planning procedures and follow-up reporting, thanks to automation, standardization and …
planning procedures and follow-up reporting, thanks to automation, standardization and …
Navigating the nuances: comparative analysis and hyperparameter optimisation of neural architectures on contrast-enhanced MRI for liver and liver tumour …
In medical imaging, accurate segmentation is crucial to improving diagnosis, treatment, or
both. However, navigating the multitude of available architectures for automatic …
both. However, navigating the multitude of available architectures for automatic …
Detection of hepatocellular carcinoma in contrast-enhanced magnetic resonance imaging using deep learning classifier: a multi-center retrospective study
Hepatocellular carcinoma (HCC) is one of the most common malignant tumors and a
leading cause of cancer-related death worldwide. We propose a fully automated deep …
leading cause of cancer-related death worldwide. We propose a fully automated deep …