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Segment anything in medical images

open access: yesNature Communications
Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability
Jun Ma   +5 more
doaj   +4 more sources

A review: Deep learning for medical image segmentation using multi-modality fusion

open access: yesArray, 2019
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 multi-information to improve the segmentation.
Tongxue Zhou, Su Ruan, Stéphane Canu
doaj   +3 more sources

Hybrid intelligence in medical image segmentation. [PDF]

open access: yesSci Rep
Abstract Medical image segmentation is vital for precise identification and analysis of anatomical structures and pathological regions, yet traditional models often fall short in aligning with clinical workflows, requiring extensive manual correction even when overall segmentation accuracy is high.
Ali NM   +4 more
europepmc   +4 more sources

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

open access: yesFrontiers in Bioengineering and Biotechnology
Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with
Guanqun Sun   +10 more
doaj   +3 more sources

Neural Architecture Search for Light-weight Medical Image Segmentation Network [PDF]

open access: yesJisuanji kexue, 2022
Most of the existing medical image segmentation models with excellent performance are manually designed by domain experts.The design process usually requires a lot of professional knowledge and repeated experiments.In addition,the over complex ...
ZHANG Fu-chang, ZHONG Guo-qiang, MAO Yu-xu
doaj   +1 more source

A Review of Medical Image Segmentation Algorithms [PDF]

open access: yesEAI Endorsed Transactions on Pervasive Health and Technology, 2021
INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is ...
K.K.D. Ramesh   +4 more
doaj   +1 more source

A Survey on Medical Image Segmentation Based on Deep Learning Techniques

open access: yesBig Data and Cognitive Computing, 2022
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent
Jayashree Moorthy, Usha Devi Gandhi
doaj   +1 more source

Fast Segmentation of Vertebrae CT Image Based on the SNIC Algorithm

open access: yesTomography, 2022
Automatic image segmentation plays an important role in the fields of medical image processing so that these fields constantly put forward higher requirements for the accuracy and speed of segmentation.
Bing Li   +4 more
doaj   +1 more source

Medical Image Segmentation Based on Transformer and HarDNet Structures

open access: yesIEEE Access, 2023
Medical image segmentation is a crucial way to assist doctors in the accurate diagnosis of diseases. However, the accuracy of medical image segmentation needs further improvement due to the problems of many noisy medical images and the high similarity ...
Tongping Shen, Huanqing Xu
doaj   +1 more source

SW-UNet: a U-Net fusing sliding window transformer block with CNN for segmentation of lung nodules

open access: yesFrontiers in Medicine, 2023
Medical images are information carriers that visually reflect and record the anatomical structure of the human body, and play an important role in clinical diagnosis, teaching and research, etc.
Jiajun Ma   +4 more
doaj   +1 more source

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