Advances in Auto-Segmentation

Cárdenas, Carlos; Yang, Jinzhong; Anderson, Brian; Court, Laurence Edward; Brock, Kristy

doi:10.1016/j.semradonc.2019.02.001

Cited by 273 publications

(255 citation statements)

References 99 publications

Supporting

Mentioning

254

Contrasting

Order By: Relevance

“…Detailed analysis of dose-volume-toxicity relationships for an array of cardiac sub-structures requires contouring with sufficient quality, a task typically performed by expert radiation oncologists. This process is time consuming (up to several hours/patient for complex structures arrays [7]) and inter-user variability and manual errors performed during the contouring process may bias results as reported by Caravatta et al [8]. It is important to highlight that the inter-observer variability is always an issue also in judgment of automatic contours generated, for example, by contour propagation techniques; these will include the potential biases too in addition to the uncertainty in the registrations.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Maffei

Fiorini

Aluisio³

et al. 2020

Physica Medica

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Maffei

Fiorini

Aluisio³

et al. 2020

Physica Medica

View full text Add to dashboard Cite

“…The use of MR imaging in radiotherapy continues to rapidly grow as this modality provides improved soft tissue contrast when compared to CT imaging. With greater use of MR imaging in radiotherapy as well as the advent of adaptive and online MR‐guided radiotherapy, there has been an increasing need for real‐time automated solutions that generate accurate segmentations resulting in the development of novel and more innovative auto‐segmentation approaches . In this work we introduce the first dedicated dataset for the evaluation of T2‐weighted MR imaging auto‐segmentation algorithms for head and neck salivary glands and lymph node levels.…”

Section: Discussionmentioning

confidence: 99%

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose The use of magnetic resonance imaging (MRI) in radiotherapy treatment planning has rapidly increased due to its ability to evaluate patient’s anatomy without the use of ionizing radiation and due to its high soft tissue contrast. For these reasons, MRI has become the modality of choice for longitudinal and adaptive treatment studies. Automatic segmentation could offer many benefits for these studies. In this work, we describe a T2‐weighted MRI dataset of head and neck cancer patients that can be used to evaluate the accuracy of head and neck normal tissue auto‐segmentation systems through comparisons to available expert manual segmentations. Acquisition and validation methods T2‐weighted MRI images were acquired for 55 head and neck cancer patients. These scans were collected after radiotherapy computed tomography (CT) simulation scans using a thermoplastic mask to replicate patient treatment position. All scans were acquired on a single 1.5 T Siemens MAGNETOM Aera MRI with two large four‐channel flex phased‐array coils. The scans covered the region encompassing the nasopharynx region cranially and supraclavicular lymph node region caudally, when possible, in the superior–inferior direction. Manual contours were created for the left/right submandibular gland, left/right parotids, left/right lymph node level II, and left/right lymph node level III. These contours underwent quality assurance to ensure adherence to predefined guidelines, and were corrected if edits were necessary. Data format and usage notes The T2‐weighted images and RTSTRUCT files are available in DICOM format. The regions of interest are named based on AAPM’s Task Group 263 nomenclature recommendations (Glnd_Submand_L, Glnd_Submand_R, LN_Neck_II_L, Parotid_L, Parotid_R, LN_Neck_II_R, LN_Neck_III_L, LN_Neck_III_R). This dataset is available on The Cancer Imaging Archive (TCIA) by the National Cancer Institute under the collection “AAPM RT‐MAC Grand Challenge 2019” (https://doi.org/10.7937/tcia.2019.bcfjqfqb). Potential applications This dataset provides head and neck patient MRI scans to evaluate auto‐segmentation systems on T2‐weighted images. Additional anatomies could be provided at a later time to enhance the existing library of contours.

show abstract

“…These include automation of treatment planning [31], adaptive radiotherapy, MR-linac systems [32], biological and functional imaging [33], dose painting [34], radiomics [35], dosiomics [36], and predictive modelling [37]. There is also a wide range of topics investigated with artificial intelligence (neural networks, deep learning [38,39]), including segmentation of tumors and OARs [40], pseudo-CT generation from MRI [41], dose prediction for treatment planning [42], patient-specific quality assurance [43], real-time respiratory motion prediction [44], and prediction of treatment response [45].…”

Section: Computational Methods and Automationmentioning

confidence: 99%

Adapting training for medical physicists to match future trends in radiation oncology

Clark

Gagliardi

Heijmen

et al. 2019

Physics and Imaging in Radiation Oncology

View full text Add to dashboard Cite

Advances in Auto-Segmentation

Cited by 273 publications

References 99 publications

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

Adapting training for medical physicists to match future trends in radiation oncology

Contact Info

Product

Resources

About