Image Segmentation Techniques: Statistical, Comprehensive, Semi-Automated Analysis and an Application Perspective Analysis of Mathematical Expressions

Sakshi, .; Kukreja, Vinay

doi:10.1007/s11831-022-09805-9

Cited by 38 publications

(7 citation statements)

References 217 publications

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“…A summary of the papers reviewed in Section 3.1 is given in Table 1 . The columns of the table contain the following information: identification of each study (column Authors); publication year (column Year); general category or approach used in the segmentation method as defined in [ 18 ] (column Segmentation Category); the specific segmentation technique or algorithm (column Method); whether the method is fully-automatic or semi-automatic (column Autom. Level for Automation Level); whether the segmentation is performed in 2D or 3D (column Dim.…”

Section: Resultsmentioning

confidence: 99%

“…Several taxonomies can be used to characterise the reviewed papers. We decided to adopt the taxonomy introduced in a recent paper by Sakshi and Kukreja (2023) [ 18 ], published in the reputable journal Archives of Computational Methods in Engineering (Impact Factor of 9.7). Figure 3 shows the selected papers categorised by image segmentation technique according to this taxonomy.…”

Section: Methodsmentioning

confidence: 99%

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Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Niño,

Bernardino,

Domingues

2024

Sensors

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Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Niño,

Bernardino,

Domingues

2024

Sensors

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“…Neurons, which are basic brain building blocks, function as the core computational units of the brain, underpinning the vast expanse of conscious and subconscious processes, and defining our neural identity with each electrochemical interaction [11]. Neurons communicate with other neurons, and non-neuronal cells like muscles and glands by biological connections called "chemical synapses", which are the communication points, at which sending nerve cells called presynaptic neurons, transmit the message to receiving nerve cells called the postsynaptic neuron.…”

Section: Neural Communicationmentioning

confidence: 99%

Section: Neural Communicationmentioning

confidence: 99%

“…However, creating a 3D model of organs and/or their abnormalities is timeconsuming and is often done manually or semi-automatically [10]. AI can automate this process and also contribute to increasing the quality of the resulting 3D objects [11,12] as well as visual content in the Metaverse [4,13]. To give the users a real sense of visual immersion, the developers should virtual objects of high quality [14].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Rudnicka,

Szczepanski,

Pregowska

2023

Preprint

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Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

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Hybrid optimized MRF based lung lobe segmentation and lung cancer classification using Shufflenet

Mahesh

2023

Multimed Tools Appl

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Image Segmentation Techniques: Statistical, Comprehensive, Semi-Automated Analysis and an Application Perspective Analysis of Mathematical Expressions

Cited by 38 publications

References 217 publications

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual-Content Generation

Hybrid optimized MRF based lung lobe segmentation and lung cancer classification using Shufflenet

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