Zobrazeno 1 - 10
of 37 611
pro vyhledávání: '"TUMOR SEGMENTATION"'
Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during resection surgery. The lack of large annotated datasets limits the current models performances. In this paper, we i
Externí odkaz:
http://arxiv.org/abs/2411.14017
Accurate segmentation of brain tumors plays a key role in the diagnosis and treatment of brain tumor diseases. It serves as a critical technology for quantifying tumors and extracting their features. With the increasing application of deep learning m
Externí odkaz:
http://arxiv.org/abs/2411.01896
In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learning models have been developed to automate this process, the focus has p
Externí odkaz:
http://arxiv.org/abs/2411.00922
Liver cancer is a leading cause of mortality worldwide, and accurate CT-based tumor segmentation is essential for diagnosis and treatment. Manual delineation is time-intensive, prone to variability, and highlights the need for reliable automation. Wh
Externí odkaz:
http://arxiv.org/abs/2410.10005
Autor:
Moradi, Nikoo, Ferreira, André, Puladi, Behrus, Kleesiek, Jens, Fatemizadeh, Emad, Luijten, Gijs, Alves, Victor, Egger, Jan
Radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex
Externí odkaz:
http://arxiv.org/abs/2411.14752
Publikováno v:
Medical Image Understanding and Analysis (MIUA), Lecture Notes in Computer Science, Springer, vol. 14859, 2024
Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a
Externí odkaz:
http://arxiv.org/abs/2411.14418
Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. However, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracti
Externí odkaz:
http://arxiv.org/abs/2411.04595
Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-ten fail to effectively incorporate medical domain knowledgesuch as tum
Externí odkaz:
http://arxiv.org/abs/2410.19847