Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy

Figures & data

Figure 1. The process of auto-segmentation consists of three steps. (1) A rigid registration between the CT scans in the atlas database and the CT scan to be auto-segmented aiming to obtain the five CT scans with the highest anatomical match. However, for auto-segmentation of the left ventricle wall, only three subjects were used as this resulted in a higher delineation consistency than when using five subjects. (2) The selected CT scans were submitted to the deformable image registration (DIR) algorithm where the suggestion for each structure for the selected patients were transferred to the new CT scan. (3) Finally, the simultaneous truth and performance level estimation (STAPLE) algorithm was used to collapse the multiple delineations into one delineation.

Figure 2. Boxplot of the DSC between the 60 manual delineations (blue dots) performed by 16 observers in CT scans of 12 breast cancer patients and between the automatic and manual delineations (red dots) for the heart and substructures in the final test. Median values for manual and automatic delineations are given at the top, with corresponding p-values for the difference. Abbreviations: Heart: Whole heart; Vent: Ventricle; L: Left; R: Right; Ant: Anterior; Lat: Lateral; Inf: Inferior; Sep: Septal; LMCA: Left Main Coronary Artery; LAD: Left Anterior Descending coronary artery; Cx: Circumflex coronary artery; RCA: Right Coronary Artery; PDA: Posterior Descending Artery; Prox: Proximal; Mid: Middle; Dist: Distal; PT: Pulmonary Trunk; SVC: Superior Vena Cava; IVC: Inferior Vena Cava.

Figure 3. Boxplot of the MSD between the 60 manual delineations, (blue dots) performed by 16 observers in CT scans of 12 breast cancer patients and between the automatic and manual delineations (red dots) for the heart and substructures in the final test. Median values for manual and automatic delineations are given at the top, with corresponding p-values for the difference. Abbreviations: Heart: Whole heart; Vent: Ventricle; L: Left; R: Right; Ant: Anterior; Lat: Lateral; Inf: Inferior; Sep: Septal; LMCA: Left Main Coronary Artery; LAD: Left Anterior Descending coronary artery; Cx: Circumflex coronary artery; RCA: Right Coronary Artery; PDA: Posterior Descending Artery; Prox: Proximal; Mid: Middle; Dist: Distal; PT: Pulmonary Trunk; SVC: Superior Vena Cava; IVC: Inferior Vena Cava.

Figure 4. The correlation between RT doses in the automatic delineations (x-axis) and the five manual delineations (y-axis) for the heart and substructures in the final test. The line is x = y (line of identity) where the light gray dots located vertically indicates the variation in the manual delineations. Abbreviations: Heart: Whole heart; Vent: Ventricle; L: Left; R: Right; Ant: Anterior; Lat: Lateral; Inf: Inferior; Sep: Septal; LMCA: Left Main Coronary Artery; LAD: Left Anterior Descending coronary artery; Cx: Circumflex coronary artery; RCA: Right Coronary Artery; PDA: Posterior Descending Artery; Prox: Proximal; Mid: Middle; Dist: Distal; PT: Pulmonary Trunk; SVC: Superior Vena Cava; IVC: Inferior Vena Cava.

Table 1. Comparison of the method, sample size, and structures for auto-segmentation using atlas-based approach published in the recent years.

Study	Method	Sample size	Chambers (DSC)	Left ventricle wall (DSC)	Coronary arteries (DSC)	Large vessels (DSC)	Evaluating
Maffei et al. [21]^a^,b	Hierarchical clustering Non-contrast CT	36 (atlas + cross-validation)	Heart (0.94) Chambers (0.76–0.87)	Five substructures^c (0.50–0.61)	LMCA (0.19) LADCA, 3 seg (0.04–0.19) RCA, 4 seg (0.06–0.16) Cx, 2 seg (0.17–0.27)	Asc aorta (0.77) PT (0.77) SVC (0.68) IVC (0.59)	Geometric (DSC, HD)
Loap et al. [22]	Multi-atlas Non-contrast CT	20 (atlas) 20 (validation)	Heart (0.95) Chambers (0.76–0.87)	Five substructures^c (0.50–0.61)	–	–	Geometric (DSC, volumes)
Morris et al. [23] ^a,d,e,f	Multi-atlas Registration with MRI/ Non-contrast CT	20 (atlas) 11 (validation)	Heart (0.95) Chambers (0.83–0.91)	–	LMCA (0.12) LADCA (0.27) RCA (0.22)	Asc aorta (0.84) PT (0.84) SVC (0.80) IVC (0.70)	Geometric (DSC, MSD, centroid displacement) Dosimetric
Kaderka et al. [19] ^a,d	Multi-atlas Non-contrast CT	27 (atlas) 27 (validation)	Heart (0.93) Chambers (0.76–0.85)	–	LADCA (0.09)	–	Geometric (DSC, HD) Dosimetric
Finnegan et al. [13,20]	Multi-atlas Leave one out Non-contrast CT	20 (atlas + cross-validation)	Heart (0.94) Chambers (0.82–0.86)	–	LMCA(0.12) LADCA (0.05) RCA (0.04) Cx (0.06)	Asc aorta (0.80) Dec aorta (0.85) PT (0.67) SVC (0.55)	Geometric [20] (DSC, MSD) Dosimetric [13]
Jung et al. [24]	Multi-atlas Leave one out Non-contrast CT	30 (atlas + cross-validation)	Heart (0.97) Chambers (0.65–0.80)	–	LMCA (NA) LADCA (NA) RCA (NA) Cx (NA)	–	Geometric (DSC, MSD) Dosimetric
Zhou et al. [25]^a	Multiatlas segmentation Contrast CT	12 (atlas) 19 (validation)	Heart (0.95) Chambers (0.76–0.81)	–	LMCA (0.24) LADCA (0.15) RCA (0.18) Cx (0.32)	Asc aorta (0.78) PT (0.82) SVC (0.66) IVC (0.64)	Geometric (DSC, MSD)
This work^a^,d	Multi-atlas Non-contrast CT	42 (atlas) 60 (validation) (12 CT-scans/5 delineations)	Heart (0.94) Chambers (0.75–0.86)	Four substructures^g (0.36–0.63)	LMCA (0.14) LADCA, 3 seg (0.09–0.21) RCA, 4 seg (0.00–0.07) Cx, 2 seg (0.00–0.07)	Asc aorta (0.76) PT (0.61) SVC (0.55) IVC (0.68)	Geometric (DSC, MSD) Dosimetric

^aSimultaneous Truth and Performance Level Estimation (STAPLE algorithm was used).

^bDSC values are reported for STAPLE algorithm.

^cDelineated according to Duane et al.

^dMIM software was used for auto segmentation.

^eThe CT scans were rigid registered with magnetic resonance imaging to improve the visibility of cardiac structures.

^fDSC values are reported for STAPLE algorithm (10 patients).

^gDelineated according to Milo et al.

Abbreviations: DSC: Dice similarity coefficient, LMCA: Left main coronary artery, LADCA: Left anterior descending coronary artery, seg: Segments, RCA: Right coronary artery, Cx: circumflex coronary artery, Asc: Ascending, PT: Pulmonary trunk, SVC: superior vena cava, IVC: Inferior vena cave, HD: Hausdorff Distance, MSD: Mean Surface Distance.

Maffei N, Fiorini L, Aluisio G, et al. Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac Sub-structures. Phys Med. 2020;69:70–80.

 PubMed Web of Science ®Google Scholar

Loap P, Tkatchenko N, Kirova Y. Evaluation of a delineation software for cardiac atlas-based autosegmentation: an example of the use of artificial intelligence in modern radiotherapy. Cancer Radiother. 2020;24(8):826–833.

 PubMed Web of Science ®Google Scholar

Morris ED, Ghanem AI, Pantelic MV, et al. Cardiac substructure segmentation and dosimetry using a novel hybrid magnetic resonance and computed tomography cardiac atlas. Int J Radiat Oncol Biol Phys. 2019;103(4):985–993.

 PubMed Web of Science ®Google Scholar

Kaderka R, Gillespie EF, Mundt RC, et al. Geometric and dosimetric evaluation of atlas based auto-segmentation of cardiac structures in breast cancer patients. Radiother Oncol. 2019;131:215–220.

 PubMed Web of Science ®Google Scholar

Finnegan R, Lorenzen EL, Dowling J, et al. Analysis of cardiac substructure dose in a large, multi-centre Danish breast cancer cohort (the DBCG HYPO trial): trends and predictive modelling. Radiother Oncol. 2020;153:130–138.

 PubMed Web of Science ®Google Scholar

Finnegan R, Dowling J, Koh ES, et al. Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework. Phys Med Biol. 2019;64(8):085006.

 PubMed Web of Science ®Google Scholar

Jung JW, Lee C, Mosher EG, et al. Automatic segmentation of cardiac structures for breast cancer radiotherapy. Phys Imaging Radiat Oncol. 2019;12:44–48.

 PubMedGoogle Scholar

Zhou R, Liao Z, Pan T, et al. Cardiac atlas development and validation for automatic segmentation of cardiac substructures. Radiother Oncol. 2017;122(1):66–71.

 PubMed Web of Science ®Google Scholar