Improving automatic segmentation of liver tumor images using a deep learning model

Song, Zhendong; Wu, Huiming; Chen, Wei; Slowik, Adam

doi:10.1016/j.heliyon.2024.e28538

Cited by 1 publication

(1 citation statement)

References 45 publications

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“…Song et al 2024 proposes an enhanced liver vessel segmentation approach utilizing a modified 3D fully convolutional neural network (V-Net), incorporating pyramidal convolution blocks and multi-resolution deep supervision for improved segmentation accuracy. Evaluation of public datasets demonstrates superior performance, particularly on the Dice Coefficient index, offering potential advancements in liver tumour treatment [39]. He et al 2023 presents MultiTrans, a framework for assisting doctors in segmenting head and neck organs at risk (OARs) with improved accuracy through a multi-scale feature fusion module.…”

Section: Cnn and Transformer Based Methodsmentioning

confidence: 99%

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Ramalakshmi,

Srinivasa Raghavan,

Rajagopal

et al. 2024

Biomed. Phys. Eng. Express

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Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensively analysed Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self-learning and decision-making.

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Section: Cnn and Transformer Based Methodsmentioning

confidence: 99%