Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Annals of Nuclear Medicine
Published in: Annals of Nuclear Medicine 9/2022

30-06-2022 | Prostate Cancer | Original Article

Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients

Authors: Kazuki Motegi, Noriaki Miyaji, Kosuke Yamashita, Mitsuru Koizumi, Takashi Terauchi

Published in: Annals of Nuclear Medicine | Issue 9/2022

Login to get access

Abstract

Objective

We aimed to compare the deep learning-based (VSBONE BSI) and atlas-based (BONENAVI) segmentation accuracy that have been developed to measure the bone scan index based on skeletal segmentation.

Methods

We retrospectively conducted bone scans for 383 patients with prostate cancer. These patients were divided into two groups: 208 patients were injected with 99mTc-hydroxymethylene diphosphonate processed by VSBONE BSI, and 175 patients were injected with 99mTc-methylene diphosphonate processed by BONENAVI. Three observers classified the skeletal segmentations as either a “Match” or “Mismatch” in the following regions: the skull, cervical vertebrae, thoracic vertebrae, lumbar vertebrae, pelvis, sacrum, humerus, rib, sternum, clavicle, scapula, and femur. Segmentation error was defined if two or more observers selected “Mismatch” in the same region. We calculated the segmentation error rate according to each administration group and evaluated the presence of hot spots suspected bone metastases in "Mismatch" regions. Multivariate logistic regression analysis was used to determine the association between segmentation error and variables like age, uptake time, total counts, extent of disease, and gamma cameras.

Results

The regions of “Mismatch” were more common in the long tube bones for VSBONE BSI and in the pelvis and axial skeletons for BONENAVI. Segmentation error was observed in 49 cases (23.6%) with VSBONE BSI and 58 cases (33.1%) with BONENAVI. VSBONE BSI tended that “Mismatch” regions contained hot spots suspected of bone metastases in patients with multiple bone metastases and showed that patients with higher extent of disease (odds ratio = 8.34) were associated with segmentation error in multivariate logistic regression analysis.

Conclusions

VSBONE BSI has a potential to be higher segmentation accuracy compared with BONENAVI. However, the segmentation error in VSBONE BSI occurred dependent on bone metastases burden. We need to be careful when evaluating multiple bone metastases using VSBONE BSI.
Literature
1.
Van den Wyngaert T, Strobel K, Kampen WU, Kuwert T, van der Bruggen W, Mohan HK, et al. The EANM practice guidelines for bone scintigraphy. Eur J Nucl Med Mol Imaging. 2016;43:1723–38.CrossRef
2.
Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Quality of planar whole-body bone scan interpretations—a nationwide survey. Eur J Nucl Med Mol Imaging. 2008;35:1464–72.CrossRef
3.
Sadik M, Jakobsson D, Olofsson F, Ohlsson M, Suurkula M, Edenbrandt L. A new computer-based decision-support system for the interpretation of bone scans. Nucl Med Commun. 2006;27:417–23.CrossRef
4.
Sadik M, Hamadeh I, Nordblom P, Suurkula M, Höglund P, Ohlsson M, et al. Computer-assisted interpretation of planar whole-body bone scans. J Nucl Med. 2008;49:1958–65.CrossRef
5.
Sadik M, Suurkula M, Höglund P, Järund A, Edenbrandt L. Improved classifications of planar whole-body bone scans using a computer-assisted diagnosis system: a multicenter, multiple-reader, multiple-case study. J Nucl Med. 2009;50:368–75.CrossRef
6.
Imbriaco M, Larson SM, Yeung HW, Mawlawi OR, Erdi Y, Venkatraman ES, et al. A new parameter for measuring metastatic bone involvement by prostate cancer: the Bone Scan Index. Clin Cancer Res. 1998;4:1765–72.PubMed
7.
International Commission on Radiological Protection. Report of the Task Group on the Reference Man, ICRP Publication 23. New York: Pergamon Press; 1975.
8.
Sabbatini P, Larson SM, Kremer A, Zhang ZF, Sun M, Yeung H, et al. Prognostic significance of extent of disease in bone in patients with androgen-independent prostate cancer. J Clin Oncol Citeseer. 1999;17:948–57.CrossRef
9.
Dennis ER, Jia X, Mezheritskiy IS, Stephenson RD, Schoder H, Fox JJ, et al. Bone scan index: a quantitative treatment response biomarker for castration-resistant metastatic prostate cancer. J Clin Oncol. 2012;30:519–24.CrossRef
10.
Knutsson H, Andersson M. Morphons: paint on priors and elastic canvas for segmentation and registration Image analysis. Berlin: Springer; 2005. p. 292–301.
11.
Sjöstrand K, Ohlsson M, Edenbrandt L. Statistical regularization of deformation fields for atlas-based segmentation of bone scintigraphy images. Med Image Comput Comput Assist Interv. 2009;12:664–71.PubMed
12.
Koizumi M, Wagatsuma K, Miyaji N, Murata T, Miwa K, Takiguchi T, et al. Evaluation of a computer-assisted diagnosis system, BONENAVI version 2, for bone scintigraphy in cancer patients in a routine clinical setting. Ann Nucl Med. 2015;29:138–48.CrossRef
13.
Koizumi M, Miyaji N, Murata T, Motegi K, Miwa K, Koyama M, et al. Evaluation of a revised version of computer-assisted diagnosis system, BONENAVI version 2.1.7, for bone scintigraphy in cancer patients. Ann Nucl Med. 2015;29:659–65.CrossRef
14.
Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: a survey. Med Image Anal. 2015;24:205–19.CrossRef
15.
Shimizu A, Wakabayashi H, Kanamori T, Saito A, Nishikawa K, Daisaki H, et al. Automated measurement of bone scan index from a whole-body bone scintigram. Int J Comput Assist Radiol Surg. 2020;15:389–400.CrossRef
16.
Higashiyama S, Yoshida A, Kawabe J. Study of the usefulness of bone scan index calculated from 99m-technetium- hydroxymethylene diphosphonate (99mTc-HMDP) bone scintigraphy for bone metastases from prostate cancer using deep learning algorithms. Curr Med Imaging Rev. 2021;17:89–96.CrossRef
17.
Aoki Y, Nakayama M, Nomura K, Tomita Y, Nakajima K, Yamashina M, et al. The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer. Ann Nucl Med. 2020;34:926–31.CrossRef
18.
Yoshida A, Higashiyama S, Kawabe J. Assessment of a software for semi-automatically calculating the bone scan index on bone scintigraphy scans. Clin Imaging. 2021;78:14–8.CrossRef
19.
Soloway MS, Hardeman SW, Hickey D, Raymond J, Todd B, Soloway S, et al. Stratification of patients with metastatic prostate cancer based on extent of disease on initial bone scan. Cancer. 1988;61:195–202.CrossRef
20.
Sekuboyina A, Rempfler M, Kukačka J, Tetteh G, Valentinitsch A, Kirschke JS, et al. (2018) Btrfly Net: vertebrae labelling with energy-based adversarial learning of local spine prior. In: 21st International conference on medical image computing and computer assisted intervention—MICCAI, Granada, Spain, Sept 16–20, 2018, Proceedings, Part IV, pp 649–57
21.
Nakajima K, Nakajima Y, Horikoshi H, Ueno M, Wakabayashi H, Shiga T, et al. Enhanced diagnostic accuracy for quantitative bone scan using an artificial neural network system: a Japanese multi-center database project. EJNMMI Res. 2013;3:83.CrossRef
22.
Kikuchi A, Onoguchi M, Horikoshi H, Sjöstrand K, Edenbrandt L. Automated segmentation of the skeleton in whole-body bone scans. Nucl Med Commun. 2012;33:947–53.CrossRef
23.
Umeda T, Koizumi M, Fukai S, Miyaji N, Motegi K, Nakazawa S, et al. Evaluation of bone metastatic burden by bone SPECT/CT in metastatic prostate cancer patients: defining threshold value for total bone uptake and assessment in radium-223 treated patients. Ann Nucl Med Springer. 2018;32:105–13.CrossRef
24.
Kanda Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant. 2013;48:452–8.CrossRef
25.
Mota JM, Armstrong AJ, Larson SM, Fox JJ, Morris MJ. Measuring the unmeasurable: automated bone scan index as a quantitative endpoint in prostate cancer clinical trials. Prostate Cancer Prostatic Dis. 2019;22:522–30.CrossRef
26.
Armstrong AJ, Anand A, Edenbrandt L, Bondesson E, Bjartell A, Widmark A, et al. Phase 3 assessment of the automated bone scan index as a prognostic imaging biomarker of overall survival in men with metastatic castration-resistant prostate cancer: a secondary analysis of a randomized clinical trial. JAMA Oncol. 2018;4:944–51.CrossRef
27.
Doan NT, de Xivry JO, Macq B. Effect of inter-subject variation on the accuracy of atlas-based segmentation applied to human brain structures. In: SPIE Medical Imaging. International Society for Optics and Photonics; 2010. p. 76231S.
28.
Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Cham: Springer; 2015. p. 234–41.
29.
Jackson P, Hardcastle N, Dawe N, Kron T, Hofman MS, Hicks RJ. Deep learning renal segmentation for fully automated radiation dose estimation in unsealed source therapy. Front Oncol. 2018;8:215.CrossRef
30.
Ichikawa H, Miwa K, Okuda K, Shibutani T, Kato T, Nagaki A, et al. Current state of bone scintigraphy protocols and practice in Japan. Asia Ocean J Nucl Med Biol. 2020;8:116–22.PubMedPubMedCentral
31.
Shibutani T, Onoguchi M, Yoneyama H, Konishi T, Nakajima K. Performance of SwiftScan planar and SPECT technology using low-energy high-resolution and sensitivity collimator compared with Siemens SPECT system. Nucl Med Commun. 2021;42:732–7.CrossRef
32.
Ljungberg M, Pretorius PH. SPECT/CT: an update on technological developments and clinical applications. Br J Radiol. 2018;91:20160402.CrossRef
33.
Koulikov V, Lerman H, Kesler M, Even-Sapir E. (99m)Tc-MDP bone scintigraphy of the hand: comparing the use of novel cadmium zinc telluride (CZT) and routine NaI(Tl) detectors. EJNMMI Res. 2015;5:63.CrossRef
Metadata
Title
Comparison of skeletal segmentation by deep learning-based and atlas-based segmentation in prostate cancer patients
Authors
Kazuki Motegi
Noriaki Miyaji
Kosuke Yamashita
Mitsuru Koizumi
Takashi Terauchi
Publication date
30-06-2022
Publisher
Springer Nature Singapore
Published in
Annals of Nuclear Medicine / Issue 9/2022
Print ISSN: 0914-7187
Electronic ISSN: 1864-6433
DOI
https://doi.org/10.1007/s12149-022-01763-3

Other articles of this Issue 9/2022

Annals of Nuclear Medicine 9/2022 Go to the issue

Review Article

Recent topics of the clinical utility of PET/MRI in oncology and neuroscience

Review Article

Advances in positron emission tomography tracers related to vascular calcification

Letter to the Editor

The safe handling of 211At compounds

Original Article

Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease

Letter to the Editor

Response to the letter to the editor

Original Article

Unusual perfusion patterns on perfusion-only SPECT/CT scans in COVID-19 patients