TY - JOUR
T1 - Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations
AU - Cardenas, Carlos E.
AU - Mohamed, Abdallah S.R.
AU - Yang, Jinzhong
AU - Gooding, Mark
AU - Veeraraghavan, Harini
AU - Kalpathy-Cramer, Jayashree
AU - Ng, Sweet Ping
AU - Ding, Yao
AU - Wang, Jihong
AU - Lai, Stephen Y.
AU - Fuller, Clifton D.
AU - Sharp, Greg
N1 - Funding Information:
Dr. Veeraraghavan’s work was partly supported by the MSK Cancer Center support grant/core grant P30 CA008748. Dr. Gooding is employed by Mirada Medical. Dr. Kalpathy‐Cramer’s work was partly supported by NCI/NIH grant U24CA180927 and Leidos contract 17X095. Dr. Cardenas receives support from NCI/NIH award 1L30 CA242657‐01. Dr. Lai receives funding and salary support as the PI from National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248). Dr. Fuller received funding and salary support related to this project from the National Institutes of Health (NIH), including: the National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and an Academic Industrial Partnership Grant (R01DE028290); NCI Early Phase Clinical Trials in Imaging and Image‐Guided Interventions Program (1R01CA218148); an NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672) and an NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007). Dr. Fuller received funding and salary support unrelated to this project from: National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679); NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) standard grant (NSF 1933369) a National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787‐01); the NIH Big Data to Knowledge (BD2K) Program of the National Cancer Institute (NCI) Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825). Direct infrastructure support was provided by the multidisciplinary Stiefel Oropharyngeal Research Fund of the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer and the Cancer Center Support Grant (P30CA016672) and the MD Anderson Program in Image‐guided Cancer Therapy.Dr. Fuller has received direct industry grant support, honoraria, and travel funding from Elekta AB. Dr. Ng receives funding from the Australian postgraduate award and RANZCR research grants.
Funding Information:
The authors would like to thank the American Association of Physicists in Medicine (AAPM), the AAPM Work Group on Grand Challenges, and the AAPM staff for their support and sponsorship of the 2019 AAPM RT-MAC Grand Challenge and The Cancer Imaging Archive (TCIA) by the National Cancer Institute for hosting the datasets and making them available to the public. Furthermore, we would like to thank Andrew Beers and Benjamin Bearce for their assistance hosting, maintaining, and continuously supporting for the grand challenge's website through the MedICI platform which was developed and supported by Massachusetts General Hospital.
Funding Information:
The authors would like to thank the American Association of Physicists in Medicine (AAPM), the AAPM Work Group on Grand Challenges, and the AAPM staff for their support and sponsorship of the 2019 AAPM RT‐MAC Grand Challenge and The Cancer Imaging Archive (TCIA) by the National Cancer Institute for hosting the datasets and making them available to the public. Furthermore, we would like to thank Andrew Beers and Benjamin Bearce for their assistance hosting, maintaining, and continuously supporting for the grand challenge's website through the MedICI platform which was developed and supported by Massachusetts General Hospital.
Publisher Copyright:
© 2019 American Association of Physicists in Medicine
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
KW - MRI
KW - automatic segmentation
KW - grand challenge
KW - head and neck cancer
KW - radiation therapy
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U2 - 10.1002/mp.13942
DO - 10.1002/mp.13942
M3 - Article
C2 - 32418343
AN - SCOPUS:85084785512
SN - 0094-2405
VL - 47
SP - 2317
EP - 2322
JO - Medical physics
JF - Medical physics
IS - 5
ER -