Artificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

Sarria, Gustavo R.; Kugel, Fabian; Roehner, Fred; Layer, Julian; Dejonckheere, Cas; Scafa, Davide; Koeksal, Muemtaz; Leitzen, Christina; Schmeel, Leonard Christopher

doi:10.1016/j.adro.2023.101394

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Thus, we advocate fully automatic methods for contour initialisation, together with the development of intuitive tools that allow specialists to modify the fully automatically generated contour if they feel the need to do so. This point of view is in line with current clinical practice, where the specialist follows contouring guidelines from respected entities such as the European Society for Radiotherapy and Oncology, the American Society for Radiation Oncology, or the Global Harmonization Group [59], while being allowed to use built-in auto-delineation and interpolation tools [60]. We therefore agree with Sarria et al [60] in that, while AI can improve the accuracy and consistency of contouring, it cannot replace the knowledge and clinical judgement of radiation oncologists, physicists, or radiation therapists.…”

Section: Resultsmentioning

confidence: 57%

“…This point of view is in line with current clinical practice, where the specialist follows contouring guidelines from respected entities such as the European Society for Radiotherapy and Oncology, the American Society for Radiation Oncology, or the Global Harmonization Group [59], while being allowed to use built-in auto-delineation and interpolation tools [60]. We therefore agree with Sarria et al [60] in that, while AI can improve the accuracy and consistency of contouring, it cannot replace the knowledge and clinical judgement of radiation oncologists, physicists, or radiation therapists. AI should be used as a tool to support and optimise clinical decision making and not as a substitute for human expertise.…”

Section: Resultsmentioning

confidence: 57%

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

Niño,

Bernardino,

Domingues

2024

Preprint

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Oncology has emerged as a crucial field of study and treatment 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 on computed tomography scans, and evaluates their accuracy and efficiency. It is noteworthy that although studies have been conducted on liver segmentation in computed tomography scans, they often lack an intuitive and visual component that allows healthcare professionals to manipulate and observe the results obtained, thereby limiting interaction with the outcomes. From the literature review, challenges such as under-segmentation, over-segmentation, and poor boundary detection, as well as the selection of methods to improve the accuracy and efficiency of liver segmentation in computed tomography scanners, are highlighted as needs to be addressed. The importance of future research in understanding the essential features for the study, generating more datasets, improving segmentation efficiency, and developing lightweight artificial intelligence frameworks for liver segmentation is outlined.

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Section: Resultsmentioning

confidence: 57%

Section: Resultsmentioning

confidence: 57%

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

Niño,

Bernardino,

Domingues

2024

Preprint

View full text Add to dashboard Cite

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Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment

Lewandowska,

Street,

Yim

et al. 2024

J of Medical Radiation Sci

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IntroductionThe application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade‐offs. A discrete choice experiment (DCE) was undertaken to examine the AI‐related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.MethodsRadiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2–4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.ResultsThe survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in‐depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost‐effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.ConclusionsThis study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI‐driven technologies in radiation therapy.

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

Niño,

Bernardino,

Domingues

2024

Sensors

View full text Add to dashboard Cite

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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Artificial Intelligence–Based Autosegmentation: Advantages in Delineation, Absorbed Dose-Distribution, and Logistics

Cited by 4 publications

References 26 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 in radiation therapy treatment planning: A discrete choice experiment

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

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