Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(7), P. e26151 - e26151

Published: July 12, 2021

Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer potentially time-saving solution, the challenges defining, quantifying, achieving expert performance remain.Adopting deep learning approach, we aim demonstrate 3D U-Net architecture that achieves expert-level delineating 21 distinct risk commonly segmented clinical practice.The model was trained on data set of 663 deidentified computed tomography scans acquired routine practice both segmentations taken from created by experienced radiographers as part research, all accordance consensus organ definitions.We demonstrated model's applicability assessing its test practice, 2 independent experts. We introduced surface Dice similarity coefficient, new metric comparison delineation, quantify deviation between contours rather than volumes, better reflecting task correcting errors automated segmentations. The generalizability then open-source sets, different centers countries training.Deep effective clinically applicable technique segmentation anatomy radiotherapy. With appropriate validation studies regulatory approvals, system could improve efficiency, consistency, safety radiotherapy pathways.

Language: Английский

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The Chinese Society of Clinical Oncology (CSCO) clinical guidelines for the diagnosis and treatment of nasopharyngeal carcinoma

Cancer Communications, Journal Year: 2021, Volume and Issue: 41(11), P. 1195 - 1227

Published: Oct. 26, 2021

Abstract Nasopharyngeal carcinoma (NPC) is a malignant epithelial tumor originating in the nasopharynx and has high incidence Southeast Asia North Africa. To develop these comprehensive guidelines for diagnosis management of NPC, Chinese Society Clinical Oncology (CSCO) arranged multi‐disciplinary team comprising experts from all sub‐specialties NPC to write, discuss, revise guidelines. Based on findings evidence‐based medicine China abroad, domestic have iteratively developed provide proper NPC. Overall, describe screening, clinical pathological diagnosis, staging risk assessment, therapies, follow‐up which aim improve

Language: Английский

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Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Radiotherapy and Oncology, Journal Year: 2020, Volume and Issue: 153, P. 55 - 66

Published: Sept. 10, 2020

Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack knowledge interpretation these AI can hold back wide-spread full deployment clinical practice. To facilitate integration radiotherapy workflow, generally applicable recommendations on implementation quality assurance (QA) are presented. For commonly used applications such as auto-segmentation, automated treatment planning synthetic computed tomography (sCT) basic concepts discussed depth. Emphasis put commissioning, case-specific routine QA needed for a methodical introduction

Language: Английский

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Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

arXiv (Cornell University), Journal Year: 2018, Volume and Issue: unknown

Published: Jan. 1, 2018

Over half a million individuals are diagnosed with head and neck cancer each year worldwide. Radiotherapy is an important curative treatment for this disease, but it requires manual time consuming delineation of radio-sensitive organs at risk (OARs). This planning process can delay treatment, while also introducing inter-operator variability resulting downstream radiation dose differences. While auto-segmentation algorithms offer potentially time-saving solution, the challenges in defining, quantifying achieving expert performance remain. Adopting deep learning approach, we demonstrate 3D U-Net architecture that achieves expert-level delineating 21 distinct OARs commonly segmented clinical practice. The model was trained on dataset 663 deidentified computed tomography (CT) scans acquired routine practice both segmentations taken from created by experienced radiographers as part research, all accordance consensus OAR definitions. We model's applicability assessing its test set CT practice, two independent experts. introduce surface Dice similarity coefficient (surface DSC), new metric comparison organ delineation, to quantify deviation between contours rather than volumes, better reflecting task correcting errors automated segmentations. generalisability then demonstrated open source datasets, different centres countries training. With appropriate validation studies regulatory approvals, system could improve efficiency, consistency, safety radiotherapy pathways.

Language: Английский

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A review of deep learning based methods for medical image multi-organ segmentation

Physica Medica, Journal Year: 2021, Volume and Issue: 85, P. 107 - 122

Published: May 1, 2021

Language: Английский

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Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Cancer Communications, Journal Year: 2021, Volume and Issue: 41(11), P. 1100 - 1115

Published: Oct. 6, 2021

Abstract Over the past decade, artificial intelligence (AI) has contributed substantially to resolution of various medical problems, including cancer. Deep learning (DL), a subfield AI, is characterized by its ability perform automated feature extraction and great power in assimilation evaluation large amounts complicated data. On basis quantity data novel computational technologies, especially DL, been applied aspects oncology research potential enhance cancer diagnosis treatment. These applications range from early detection, diagnosis, classification grading, molecular characterization tumors, prediction patient outcomes treatment responses, personalized treatment, automatic radiotherapy workflows, anti‐cancer drug discovery, clinical trials. In this review, we introduced general principle summarized major areas application for discussed future directions remaining challenges. As adoption AI use increasing, anticipate arrival AI‐powered care.

Language: Английский

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HmsU-Net: A hybrid multi-scale U-net based on a CNN and transformer for medical image segmentation

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 170, P. 108013 - 108013

Published: Jan. 22, 2024

Language: Английский

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Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Medical Physics, Journal Year: 2020, Volume and Issue: 47(9)

Published: June 8, 2020

Radiotherapy (RT) is one of the basic treatment modalities for cancer head and neck (H&N), which requires a precise spatial description target volumes organs at risk (OARs) to deliver highly conformal radiation dose tumor cells while sparing healthy tissues. For this purpose, OARs have be delineated segmented from medical images. As manual delineation tedious time‐consuming task subjected intra/interobserver variability, computerized auto‐segmentation has been developed as an alternative. The field imaging RT planning experienced increased interest in past decade, with new emerging trends that shifted H&N OAR atlas‐based deep learning‐based approaches. In review, we systematically analyzed 78 relevant publications on region 2008 date, provided critical discussions recommendations various perspectives: image modality — both computed tomography magnetic resonance are being exploited, but potential latter should explored more future; spinal cord, brainstem, major salivary glands most studied OARs, additional experiments conducted several less soft tissue structures; database databases corresponding ground truth currently available methodology evaluation, augmented data multiple observers institutions; current methods learning auto‐segmentation, expected become even sophisticated; guidelines followed participation experts institutions recommended; performance metrics Dice coefficient standard volumetric overlap accompanied least distance metrics, combined clinical acceptability scores assessments; segmentation best performing achieve clinically acceptable however, dosimetric impact also provide endpoints planning.

Language: Английский

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The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients

Physics in Medicine and Biology, Journal Year: 2021, Volume and Issue: 66(18), P. 185012 - 185012

Published: Aug. 27, 2021

To investigate the impact of training sample size on performance deep learning-based organ auto-segmentation for head-and-neck cancer patients, a total 1160 patients with who received radiotherapy were enrolled in this study. Patient planning CT images and regions interest (ROIs) delineation, including brainstem, spinal cord, eyes, lenses, optic nerves, temporal lobes, parotids, larynx body, collected. An evaluation dataset 200 randomly selected combined Dice similarity index to evaluate model performances. Eleven datasets different sizes from remaining 960 form models. All models used same data augmentation methods, network structures hyperparameters. A estimation based inverse power law function was established. Different change patterns found organs. Six organs had best 800 samples others achieved their 600 or 400 samples. The benefit increasing gradually decreased. Compared performance, nerves lenses reached 95% effect at 200, other 40. For fitting function, fitted root mean square errors all ROIs less than 0.03 (left eye: 0.024, others: <0.01), theRsquare except body greater 0.5. has significant auto-segmentation. relationship between depends inherent characteristics organ. In some cases, relatively small can achieve satisfactory performance.

Language: Английский

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Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation

BioMedical Engineering OnLine, Journal Year: 2024, Volume and Issue: 23(1)

Published: June 8, 2024

Abstract Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step computer-aided diagnosis, surgical navigation, radiation therapy. In past few years, with a data-driven feature extraction approach end-to-end training, automatic deep learning-based multi-organ methods have far outperformed traditional become new research topic. This review systematically summarizes latest this field. We searched Google Scholar for papers published January 1, 2016 to December 31, 2023, using keywords “multi-organ segmentation” “deep learning”, resulting 327 papers. followed PRISMA guidelines paper selection, 195 studies were deemed be within scope review. summarized two main aspects involved segmentation: datasets methods. Regarding datasets, we provided overview existing public conducted in-depth analysis. Concerning methods, categorized approaches into three major classes: fully supervised, weakly supervised semi-supervised, based on whether they require complete label information. achievements these terms accuracy. discussion conclusion section, outlined current trends segmentation.

Language: Английский

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