Beyond Segmentation: Enriching ImageCAS with Coronary Ostia Landmarks, Rooted Centerline Graphs, and Open-Weight Localization Models

Matteo Leccardi; Andrea Moglia; Marco Morandini; Michele Brembilla; Alessandro Nicosia; Andrea Penserini; Christian Brandini; Ender Konukoglu

eess.IVNov 6, 2026

Beyond Segmentation: Enriching ImageCAS with Coronary Ostia Landmarks, Rooted Centerline Graphs, and Open-Weight Localization Models

Authors: Matteo Leccardi, Andrea Moglia, Marco Morandini, Michele Brembilla, Alessandro Nicosia, Andrea Penserini, Christian Brandini, Ender Konukoglu,

Organizations: Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy · Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, 8092 Zurich, Switzerland · Department of Industrial and Information Engineering, University of Pavia, Via A. Ferrata 5, 27100 Pavia, Italy

Abstract

Coronary ostia and centerlines are key reference points for automated analysis of coronary computed tomography angiography (CCTA), from surgical planning to intraoperative vessel tracking. However, the only public ostium annotations cover 77 non-gated aortic scans, not CCTA, while CAT08 and ASOCA (32 and 40 CCTA scans) mark the ostia only implicitly, as centerline start points. This work enriches ImageCAS, the largest public dataset for coronary artery segmentation, with ostium annotations for its 1000 patients and centerline graphs rooted at these new ostia, making it, to the authors' knowledge, the largest public dataset for ostium localization and the only one with centerlines following the official ImageCAS masks and starting at annotated ostia. Three annotators marked each ostium in the first 500 volumes; in the next 500, the candidates of a model trained on the first batch were corrected (human-in-the-loop). Six 3D localization models, trained on ImageCAS, CAT08 and ASOCA, were benchmarked on 58 held-out volumes (50 ImageCAS, 4 CAT08, 4 ASOCA). SwinUNETR-V2 and nnResUNet localized all ostia without failure (>5 mm error), with median errors of 0.70-0.87 mm. Trained on CAT08 and ASOCA alone, SwinUNETR-V2 fails on 34% of right and 53% of left ostia, whereas adding 250 ImageCAS volumes removes all failures. The centerlines were extracted by a deterministic, quality-controlled minimal-path procedure; on ASOCA, they cover a median of 99.5% of the reference length, with a median per-patient mean distance of 0.22 mm between matched reference points and centerlines. Annotations, centerlines, code, and weights are available at https://hal9000-lab.github.io/.

Figures & tables

Figure 1: Overview of this work: the public CAT08, ASOCA, and ImageCAS datasets and their reconciled coronary ostium definitions; the two-stage annotation protocol used to extend ImageCAS with left/right ostium landmarks and centerline graphs; the shared benchmark of localization models trained on the combined dataset; and the resulting public release.

Figure 2: Centerline extraction pipeline. (1) Inputs: the coronary segmentation mask and the annotated left and right ostia (orange dots), resampled to a 0.5 mm isotropic grid and cropped to the arteries. (2) Centered cost: the cost of Eq. ( 1 ) decreases steeply with the distance DT from the vessel wall, so that minimal paths are both short and central; each ostium is bridged to the mask. (3) Ostium fronts: a minimal-cost front propagated from each ostium gives the geodesic distance $G$ and the minimal path (predecessor map) of every voxel, and splits the mask between the left (L) and right (R) trees according to the ostium that reaches each voxel; mask gaps of up to 10 mm are bridged. (4) Tree growth: the uncovered voxel farthest from the ostium (star) is traced back to the tree along its minimal path, which is kept as a new branch only if it adds at least 5 mm outside the region already covered (cross: rejected path), so that the result is a tree by construction. The growth is then repeated in a second pass with the wall distance capped at 1.2 times the local vessel radius (bottom arrow), which keeps the paths on the vessel axis at bulky bifurcations. (5) Centerline graph: the tips are trimmed by 2 mm and the trees smoothed branch by branch and resampled every 0.5 mm, yielding one tree per side, rooted at its ostium (square), with endpoints (circles), bifurcations, and per-node position, lumen radius, topological type and side, stored as one graph per patient. Full details are given in Appendix A.3 .

	Anatomical	Interoperator	Failure
Right ostium	1.08	1.85	5.0
Left ostium	1.45	2.59	5.0

Table 1: Distance thresholds (mm) delimiting the accuracy buckets.

Model	Developer	Architecture	Parameters
SwinUNETR-V2	NVIDIA	Swin ViT + Conv.	18.35M
ResNet50	Microsoft	Conv.	49.70M
ResNet101	Microsoft	Conv.	92.28M
nnResUNet	ACVL	Conv.	32.69M
MedNeXt-S	MIC-DKFZ	ConvNeXt	5.55M
MedNeXt-B	MIC-DKFZ	ConvNeXt	10.53M

Table 2: Specifications of the models evaluated on the test dataset.

Annotator	Left ostium		Right ostium		Combined (Left+Right)
Annotator	Mean $\pm$ SD	Median [IQR]	Mean $\pm$ SD	Median [IQR]	Mean $\pm$ SD	Median [IQR]
Annotator 1	2.89 $\pm$ 0.95	2.73 [2.35–3.40]	1.98 $\pm$ 0.78	1.90 [1.47–2.40]	2.44 $\pm$ 0.98	2.37 [1.79–2.92]
Annotator 2	2.06 $\pm$ 0.95	1.99 [1.39–2.48]	1.78 $\pm$ 0.95	1.63 [1.19–2.22]	1.92 $\pm$ 0.96	1.75 [1.25–2.37]
Annotator 3	1.73 $\pm$ 0.88	1.54 [1.10–2.22]	1.29 $\pm$ 0.71	1.19 [0.78–1.61]	1.51 $\pm$ 0.83	1.34 [0.94–1.95]
Annotator 4	1.39 $\pm$ 0.90	1.19 [0.75–1.80]	1.13 $\pm$ 0.61	1.04 [0.71–1.42]	1.26 $\pm$ 0.78	1.09 [0.72–1.57]
Annotator 5	1.44 $\pm$ 0.83	1.29 [0.81–1.94]	1.19 $\pm$ 0.67	1.10 [0.69–1.55]	1.31 $\pm$ 0.76	1.20 [0.76–1.69]

Table 3: Per-annotator localization error (Euclidean distance, in mm, between each annotator’s manually placed point and the mean of the points placed on the same image by the other two annotators, i.e. leave-one-out) for the left and right coronary ostia, individually and combined, together with the pooled statistics across all six annotators. For each group we report the mean

\pm

SD and the median with interquartile range (25th–75th percentile). The statistics are computed over 3000 placements by the six annotators (1500 landmarks per side, 250 per annotator per ostium, three annotators per volume).

Metric	Value
Images requiring manual refinement a	88 / 500 (17.6%)
Images with an undetected ostium (outlier)	10 / 500 (2.0%)
Right ostium	8
Left ostium	2
Both ostia	0
Annotation time, first batch b	$\approx$ 24 h (144 person-h)

Table 4: Semi-automated correction statistics for the second batch of 500 ImageCAS images (patients 501–1000), annotated using SwinUNETR-V2 predictions followed by single-annotator visual review.

	Right ostium					Left ostium					Both
Model	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
SwinUNETR-V2	0.53	0.74	0.83	1.02	2.07	0.61	0.85	0.98	1.32	2.51	0.68	0.86	0.91	1.10	1.94	55.6
	(O: 77.59 \| G: 20.69 \| A: 1.72 \| F: 0.00)					(O: 84.48 \| G: 15.52 \| A: 0.00 \| F: 0.00)					(O: 68.97 \| G: 29.31 \| A: 1.72 \| F: 0.00)
ASOCA	0.53	0.95	0.95	1.37	1.45	0.54	0.60	0.58	0.64	0.71	0.58	0.81	0.77	0.99	1.02
	(O: 50.00 \| G: 50.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)					(O: 50.00 \| G: 50.00 \| A: 0.00 \| F: 0.00)
CAT08	0.60	1.02	1.04	1.46	1.75	0.59	1.04	1.20	1.64	2.25	0.98	1.05	1.12	1.20	1.46

Table 5: Euclidean distance

d_{c}

(mm) of the benchmark models trained on the complete ImageCAS dataset (the 950 CCTAs not in the test set) and tested on 58 CCTAs, for the right ostium, the left ostium, and both (per-patient average of the right and left distances). The line below each row gives the percentage of cases in each accuracy bucket (for both, at patient level): O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 3 — Figure 3: Comparison of inter-annotator variability (distance of each annotator’s marker from the mean of the other two annotators of the same image, as in Table 3 , on the 50 ImageCAS test volumes) against the benchmark models’ localization error on the test set (Table 5 ), both ostia combined.

	Both trees	Left tree	Right tree
Recall (%)	99.5 [98.9–99.8]	99.4 [98.7–99.8]	99.7 [99.0–99.9]
Precision (%)	98.0 [96.5–99.6]	98.1 [95.7–99.8]	99.4 [97.7–100]
OV (%)	98.6 [98.2–99.0]	98.5 [97.6–99.1]	99.1 [98.4–99.5]
OT (%)	99.1 [98.8–99.9]	99.0 [98.4–100]	100 [99.0–100]
OF (%)	99.1 [96.8–99.5]	99.0 [96.3–99.6]	99.5 [98.6–99.9]
AI (mm)	0.22 [0.21–0.24]	0.22 [0.21–0.25]	0.22 [0.20–0.24]

Table 6: Agreement between the centerlines extracted from the 40 annotated ASOCA masks and the ASOCA reference centerlines, for both trees together and for each tree, as median [interquartile range] over the patients. Recall: fraction of the reference length matched (coverage); precision: fraction of the extracted length matched; OV, OT, OF: overlap, overlap of vessels at least 1.5 mm wide, and overlap until first error (first 40 mm of every path ignored); AI: average inside, the mean distance of the matched reference points from the extracted centerline; HD95: 95th-percentile symmetric Hausdorff distance; length ratio: extracted over reference length; radius error: extracted minus reference radius on matched points (signed mean) and its mean absolute value; endpoints: one-to-one pairing within 10 mm; bifurcations: fraction of reference bifurcations paired within 5 mm.

	Both trees	Left tree	Right tree
ImageCAS-X trees: connectivity
Trees with detached segments (%)	15.6	28.6	2.5
Detached length, in those trees (%)	47.2 [36.1–58.5]	47.7 [38.2–59.2]	24.9 [21.1–36.9]
Detached length, all trees (%)	9.7	14.6	1.0
Detached length bridged by the mask (%)	97.7	97.6	100
ImageCAS-X start points against our ostia

Table 7: Our centerlines compared with those released with ImageCAS-X, on the 800 scans the two datasets share. Median with its interquartile range below, or percentage of the trees or of the pooled length. Detached length : part of a tree that cannot be reached from the start point when segments closer than 1 mm are joined; bridged by the mask : share of it that the ImageCAS-X mask connects (a gap of one voxel allowed). Start point : ImageCAS-X start point against our annotated ostium (interoperator threshold: Table 1 ). Inside the other masks : share of the centerline length lying within one voxel of the other set’s masks. Tree size : our extraction on the official and on the ImageCAS-X masks of the same patients (all differences significant, paired Wilcoxon signed-rank test,

p<0.01

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

	Spacing (mm)	Size (with labels)	Resolution	N° of slices
ImageCAS [ 21 ]	0.25 - 0.45	1000	512 $\times$ 512	206 - 275
CAT08 [ 28 ]	0.262 - 0.408	32	512 $\times$ 512	229 - 351
ASOCA [ 18 ]	0.625	40	512 $\times$ 512	200

Appendix

Table A.1: Comparison of the raw image characteristics of the CAT08, ASOCA and ImageCAS datasets. Spacing: in-plane pixel spacing for ImageCAS and CAT08; for ASOCA, the slice spacing, which is its largest spacing (in-plane 0.3–0.4 mm).

Figure A.1: Distance from the vessel wall, $D$ , on which the cost of the minimal paths is built, for one ImageCAS patient: maximum projections along the superior–inferior (top) and anterior–posterior (bottom) axes, in millimeters from the corner of the volume cropped around the arteries. $D$ peaks along the axis of every vessel, which the minimal paths therefore follow; it is largest in the proximal vessels and at their junctions, which motivates the cap applied in the second growth pass.

Panel of Figure 13 — Figure A.2: Flattening of the coronary trees of one ImageCAS patient (343), seen from superior (anterior up, patient’s right on the right). (a) Centerline graph projected on the axial plane: vessels running toward the apex are foreshortened and branches overlap. (b) Epicardial unfolding: the trees are mapped from a spheroid fitted around the heart onto a plane centered on the aortic root; vessels no longer overlap, but distances across the meridians are stretched toward the apex (bottom of the right tree). (c) Length-preserving flattening: each tree is rebuilt from its ostium with the directions of (b) and the true length of every segment. Black lines: centerline graph; green dots: ostia (right with a red, left with a blue outline); red dots: endpoints. The vessels (gray) are drawn as tubes of the lumen radius stored in the graph, with a slightly irregular outline added for display, not from the segmentation masks. Scale bar: 20 mm. Flattened views of this kind were also used in the visual review of the released centerlines, to assess the quality of the extraction and the defects of the segmentation masks. See Appendix A.5 .

Annotator	Left ostium		Right ostium		Combined (Left+Right)
Annotator	Mean $\pm$ SD	Median [IQR]	Mean $\pm$ SD	Median [IQR]	Mean $\pm$ SD	Median [IQR]
Annotator 1	1.93 $\pm$ 0.64	1.82 [1.56–2.28]	1.32 $\pm$ 0.52	1.27 [0.98–1.60]	1.63 $\pm$ 0.66	1.58 [1.19–1.95]
Annotator 2	1.37 $\pm$ 0.64	1.33 [0.93–1.65]	1.19 $\pm$ 0.63	1.09 [0.79–1.49]	1.28 $\pm$ 0.64	1.16 [0.83–1.58]
Annotator 3	1.15 $\pm$ 0.59	1.03 [0.73–1.47]	0.86 $\pm$ 0.47	0.79 [0.52–1.08]	1.00 $\pm$ 0.55	0.90 [0.63–1.30]
Annotator 4	0.93 $\pm$ 0.60	0.79 [0.50–1.20]	0.75 $\pm$ 0.41	0.70 [0.48–0.95]	0.84 $\pm$ 0.52	0.73 [0.48–1.05]
Annotator 5	0.96 $\pm$ 0.56	0.86 [0.54–1.29]	0.79 $\pm$ 0.45	0.74 [0.46–1.04]	0.88 $\pm$ 0.51	0.80 [0.50–1.13]

Appendix

Table A.2: Per-annotator distance (Euclidean distance, in mm) between each annotator’s manually placed point and the released consensus of that image, i.e. the mean of the points of all its annotators, the annotator’s own point included, for the left and right coronary ostia, individually and combined, together with the pooled statistics across all six annotators, computed over the same 3000 placements (1500 landmarks per side, 250 per annotator per ostium, three annotators per volume, two ostia per volume - left and right). Same layout and statistics as Table 3 , which reports the leave-one-out distances; on an image annotated by three annotators each distance here is exactly two thirds of the corresponding leave-one-out distance.

	Right ostium					Left ostium
Model	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
SwinUNETR-V2	0.63	0.82	10.3	1.21	150.0	0.65	0.94	5.17	1.31	131.6	50.7
	(O: 65.52 \| G: 24.14 \| A: 1.72 \| F: 8.62)					(O: 77.59 \| G: 18.97 \| A: 0.00 \| F: 3.45)
ASOCA	0.91	1.05	1.18	1.32	1.88	1.29	1.72	29.2	29.7	112.8
	(O: 50.00 \| G: 25.00 \| A: 25.00 \| F: 0.00)					(O: 25.00 \| G: 50.00 \| A: 0.00 \| F: 25.00)
CAT08	98.3	118.6	121.2	141.5	150.0	0.82	1.50	33.8	34.5	131.6

Appendix

Table B.1: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 trained with the first 500 annotated ImageCAS images. Means and percentiles include the failed localizations. The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 .

Figure C.1: Right and left coronary ostium positions of all three datasets, after removing each patient’s own absolute position: every ostium is expressed relative to that patient’s own right-left midpoint, then rotated into a common frame whose

x

axis is the population-average left-to-right direction and whose

y

z

axes are the anatomical anterior/superior directions made orthogonal to it. Top: coronal-like projection (

x

y

). Bottom: sagittal-like projection (

x

z

). ASOCA (triangles) and CAT08 (squares) are shown as raw points; ImageCAS (circles, semi-transparent) additionally shows, for each side and projection, a 2

\sigma

confidence ellipse computed from the ImageCAS points only. Its axes are the principal axes (principal component analysis) of the covariance of the projected points, and its semi-axes are twice the standard deviation along them, so it is an ellipse, not a circle, because the spread of the points differs between directions; it encloses about 86% of a bivariate normal distribution. Right ostium in firebrick, left ostium in navy.

	Right ostium					Left ostium
ImageCAS images	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
0	1.18	1.93	34.53	53.89	170.58	2.08	36.89	50.94	99.01	139.27	48.7
	(O: 24.14 \| G: 22.41 \| A: 18.97 \| F: 34.48)					(O: 18.97 \| G: 13.79 \| A: 13.79 \| F: 53.45)
ASOCA	0.64	1.17	15.79	16.31	60.34	2.94	3.25	21.07	21.39	75.31
	(O: 50.00 \| G: 25.00 \| A: 0.00 \| F: 25.00)					(O: 0.00 \| G: 25.00 \| A: 50.00 \| F: 25.00)
CAT08	1.16	16.31	43.08	58.24	138.70	0.94	2.04	32.58	33.68	125.74

Appendix

Table D.1: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 trained with an increasing number of ImageCAS images (training and validation sets combined; 1000 denotes the complete dataset, i.e., the 950 images not in the test set), always together with the whole of CAT08 and ASOCA (excluding the held-out cases of the test set). All the models are evaluated on the same test set. The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 18 — Figure D.1: Euclidean distance $d_{c}$ (mm) of SwinUNETR-V2 vs. the number of ImageCAS images used for training (equidistant axis; 1000 denotes the complete dataset, i.e., the 950 images not in the test set; CAT08 and ASOCA always included). Top: medians (lines) and p25–p75 (bands) of the right and left ostium over the whole test set, on a logarithmic axis. Bottom: percentage of failed localizations (error above 5 mm). See Section 3.8 .

	Right ostium					Left ostium
Spacing (mm)	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
0.5	0.53	0.74	0.83	1.02	2.07	0.61	0.85	0.98	1.32	2.51	55.6
	(O: 77.59 \| G: 20.69 \| A: 1.72 \| F: 0.00)					(O: 84.48 \| G: 15.52 \| A: 0.00 \| F: 0.00)
ASOCA	0.53	0.95	0.95	1.37	1.45	0.54	0.60	0.58	0.64	0.71
	(O: 50.00 \| G: 50.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)
CAT08	0.60	1.02	1.04	1.46	1.75	0.59	1.04	1.20	1.64	2.25

Appendix

Table D.2: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 trained and tested at the isotropic spacing (mm) reported in the first column. The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 20 — Figure D.2: Euclidean distance $d_{c}$ (mm) of SwinUNETR-V2 trained and tested at the same isotropic spacing (equidistant axis). Top: medians (lines) and p25–p75 (bands) of the right and left ostium over the whole test set. Bottom: processing time per patient. See Section 3.8 .

	Right ostium					Left ostium
Spacing (mm)	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
0.5	0.52	0.70	0.83	1.13	2.03	0.68	0.87	0.96	1.21	2.32	41.1
	(O: 72.41 \| G: 24.14 \| A: 3.45 \| F: 0.00)					(O: 89.66 \| G: 10.34 \| A: 0.00 \| F: 0.00)
ASOCA	0.61	0.67	0.77	0.83	1.20	0.58	0.74	0.67	0.83	1.06
	(O: 75.00 \| G: 25.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)
CAT08	0.68	1.22	1.17	1.71	1.74	1.03	1.17	1.05	1.20	1.23

Appendix

Table D.3: Euclidean distance

d_{c}

(mm) of nnResUNet trained and tested at the isotropic spacing (mm) reported in the first column. The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 22 — Figure D.3: Euclidean distance $d_{c}$ (mm) of nnResUNet trained and tested at the same isotropic spacing (equidistant axis). Top: medians (lines) and p25–p75 (bands) of the right and left ostium over the whole test set. Bottom: processing time per patient. See Section 3.8 .

	Right ostium					Left ostium
Inference spacing (mm)	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
0.5	0.53	0.74	0.83	1.02	2.07	0.61	0.85	0.98	1.32	2.51	55.6
	(O: 77.59 \| G: 20.69 \| A: 1.72 \| F: 0.00)					(O: 84.48 \| G: 15.52 \| A: 0.00 \| F: 0.00)
ASOCA	0.53	0.95	0.95	1.37	1.45	0.54	0.60	0.58	0.64	0.71
	(O: 50.00 \| G: 50.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)
CAT08	0.60	1.02	1.04	1.46	1.75	0.59	1.04	1.20	1.64	2.25

Appendix

Table D.4: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 trained at 0.5 mm and tested at the isotropic spacing (mm) reported in the first column. The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 24 — Figure D.4: Euclidean distance $d_{c}$ (mm) of SwinUNETR-V2 trained at 0.5 mm vs. inference spacing (equidistant axis, logarithmic distance). Lines: medians; bands: p25–p75 over the whole test set. Numbers at the bottom: p75, median (bold) and p25 of the right (red), left (blue) and both (gray) ostia. See Section 3.8 .

	Right ostium					Left ostium
Window (vox)	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
5	0.59	0.77	0.84	1.00	2.04	0.63	0.86	0.98	1.33	2.54	55.8
	(O: 77.59 \| G: 20.69 \| A: 1.72 \| F: 0.00)					(O: 84.48 \| G: 15.52 \| A: 0.00 \| F: 0.00)
ASOCA	0.56	0.94	0.95	1.33	1.43	0.53	0.60	0.58	0.65	0.70
	(O: 50.00 \| G: 50.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)
CAT08	0.70	1.08	1.07	1.45	1.74	0.59	1.00	1.19	1.60	2.29

Appendix

Table D.5: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 for different sizes of the centroid window (voxels; default 11). The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 26 — Figure D.5: Euclidean distance $d_{c}$ (mm) of SwinUNETR-V2 vs. centroid window size (equidistant axis; default 11). Lines: medians; bands: p25–p75 over the whole test set. Numbers at the bottom: p75, median (bold) and p25 of the right (red), left (blue) and both (gray) ostia. See Section 3.8 .

	Right ostium					Left ostium
Overlap (vox)	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max	Time (s)
SwinUNETR-V2
0	0.52	0.77	0.86	1.09	2.09	0.67	0.97	1.12	1.46	3.02	30.1
	(O: 72.41 \| G: 20.69 \| A: 6.90 \| F: 0.00)					(O: 74.14 \| G: 22.41 \| A: 3.45 \| F: 0.00)
ASOCA	0.66	0.85	0.81	1.00	1.10	0.57	0.64	0.61	0.68	0.75
	(O: 75.00 \| G: 25.00 \| A: 0.00 \| F: 0.00)					(O: 100.00 \| G: 0.00 \| A: 0.00 \| F: 0.00)

Appendix

Table D.6: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 and nnResUNet for different overlaps between adjacent sliding-window patches at inference (voxels, with

128^{3}

patches; default 64). The line below each row gives the percentage of cases in each accuracy bucket for the right and left ostium: O = Optimal, G = Good, A = Acceptable, F = Failed. See Section 3.8 . In each distance column, the best value is in bold and the second best is underlined, comparing the rows of the same kind (overall and, separately, each dataset).

Panel of Figure 28 — Figure D.6: Euclidean distance $d_{c}$ (mm) of SwinUNETR-V2 and nnResUNet vs. sliding-window overlap at inference (equidistant axis; default 64 voxels, $128^{3}$ patches). Lines: medians; bands: p25–p75 over the whole test set. Numbers at the bottom: p75, median (bold) and p25 of the right (red), left (blue) and both (gray) ostia. See Section 3.8 .

	Right ostium					Left ostium
	p25	Med.	Mean	p75	Max	p25	Med.	Mean	p75	Max
Trained on ImageCAS only, all external volumes
SwinUNETR-V2
ASOCA (40)	0.90	1.18	1.27	1.55	2.65	0.67	1.03	4.05	1.35	118.1
	(O: 40.00 \| G: 47.50 \| A: 12.50 \| F: 0.00)					(O: 80.00 \| G: 12.50 \| A: 5.00 \| F: 2.50)
CAT08 (32)	0.61	0.77	0.96	1.20	2.65	0.74	0.89	1.18	1.41	3.49

Appendix

Table D.7: External validation: Euclidean distance

d_{c}

(mm) of SwinUNETR-V2 and nnResUNet trained on the ImageCAS images only (training and validation sets), with no volume of ASOCA or CAT08 used for training, validation or model selection. Upper part: all the volumes of ASOCA (40) and CAT08 (32), and the 50 ImageCAS test volumes as the in-domain reference. Lower part: the 8 volumes of ASOCA (4) and CAT08 (4) that belong to the test set of the benchmark, compared with the models of the benchmark trained on the three datasets (Table 5 ); these 8 volumes were not used for training by either kind of model. The line below each row gives the share of cases in each accuracy bucket (O = Optimal, G = Good, A = Acceptable, F = Failed).

	Right ostium		Left ostium		Both
Method	Mean	Med.	Mean	Med.	Mean	Med.
Zheng et al. [42] Private (1360)	1.73	1.31	1.66	1.45	1.70	-
Jang and Jeon [23] CAT08	1.94	1.80	2.22	1.70	2.08	1.59
Noothout et al. [22] Private (672)	-	1.45	-	1.55	-	-
Gajowczyk et al. [20] Private (201)	1.29	0.98	1.18	1.14	1.24	-
Leccardi et al. [44] ASOCA (40), CAT08 (32)	5.28	4.16	10.57	7.79	7.93	5.24

Appendix

Table E.1: Euclidean distance

d_{c}

(mm), as mean and median (Med.), reported in the manuscripts of previously published methods for coronary ostium localization. The data used by each work, which differ from ours and are mostly private, are indicated in gray below the reference (number of volumes in brackets). The methods were not reimplemented and the values are copied from the respective works; a dash indicates a statistic that the work does not report. Both: combined right and left values. The last row is our earlier published model, evaluated on its own test split.

	Both trees	Left tree	Right tree
Recall (%)	100 [100–100]	100 [100–100]	100 [100–100]
Precision (%)	99.9 [99.8–100]	99.9 [99.7–100]	100 [100–100]
OV (%)	99.9 [99.9–100]	99.9 [99.8–100]	100 [100–100]
OF (%)	100 [100–100]	100 [100–100]	100 [100–100]
AI (mm)	0.23 [0.22–0.24]	0.23 [0.22–0.25]	0.22 [0.21–0.24]
HD95 (mm)	0.50 [0.47–0.53]	0.50 [0.46–0.55]	0.48 [0.44–0.52]

Appendix

Table F.1: Agreement between the centerlines extracted from the 800 ImageCAS-X masks and the ImageCAS-X centerlines, for both trees together and for each tree, as median [interquartile range] over the patients. The metrics are those of Table 6 ; the overlap until first error and the bifurcation score are computed only over the patients in which they are defined (all of them for both trees together). The overlap of wide vessels and the radius errors are not reported, since the ImageCAS-X centerlines carry no radius.

Joining tolerance (mm)	Scans	Left trees	Right trees
1	30.4 (243)	28.6 (229)	2.5 (20)
2	18.1 (145)	17.3 (138)	1.3 (10)
3	5.4 (43)	5.1 (41)	0.4 (3)
5	0.6 (5)	0.6 (5)	0 (0)
10	0.1 (1)	0.1 (1)	0 (0)

Appendix

Table F.2: ImageCAS-X trees that remain split into pieces when the ends of their segments closer than the joining tolerance are joined, as a percentage (number) of the 800 scans and of the 800 left and 800 right trees.

Medical Image Localization

Medical image localization finds anatomical landmarks, lesions, organs, or devices in scans and videos to support diagnosis and image-guided care.

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ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography
Aug 31, 2026Kit M. Bransby, Esther Øksnebjerg, Kristoffer Kjær +7Medical Image Segmentation Coronary Artery Segmentation
DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark · Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark
CORA: Generalizable coronary artery disease assessment and risk stratification from coronary CT angiography using pathology-centric representation learning
Mar 25, 2026Jinkui Hao, Gorkem Durak, Halil Ertugrul Aktas +4Synthetic Data Pretraining Coronary Angiography
Department of Radiology, Northwestern University, Chicago, IL, USA · Department of Biomedical Engineering, Northwestern University, Evanston, IL, USA · Department of Cardiology, Northwestern University, Chicago, IL, USA +1
Beyond Volume Overlap: Surface Matching for Topology-Aware Coronary Artery Segmentation
Sep 22, 2026Rafael Velasquez, Esther Puyol-Antón, Pablo ArbeláezCoronary Artery Segmentation Boundary-Aware Segmentation
Universidad de los Andes, Bogotá, Colombia · School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK

Aug 31, 2026J/K move · Enter open · S save

ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography

Kit M. Bransby, Esther Øksnebjerg, Kristoffer Kjær, Jacob Kirkeby+6

DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark · Cardiovascular Research Unit, Copenhagen University Hospital – Rigshospitalet, Copenhagen, Denmark

Medical Image Segmentation Coronary Artery Segmentation Computed Tomography Medical Image Benchmarks

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Accurate segmentation of the coronary vessel lumen is a prerequisite for quantitative assessment of atherosclerotic plaque and perivascular adipose tissue in coronary computed tomography angiography (CCTA). Cardiologists rely on semi-automated methods for this task because manual vessel tracing and segmentation are labour-intensive. Although many automated methods have been proposed, their validation remains limited by the lack of large, high-quality publicly available datasets. We provide a new dataset of voxel-wise annotations of the vessel lumen and coronary segments, alongside centerlines, and mesh surfaces for 800 scans from the publicly available ImageCAS dataset. Using this dataset, we benchmark established lumen segmentation methods against inter-observer variability, stratifying performance by disease, image quality, coronary dominance, coronary segment, vessel diameter, and lumen attenuation. These labels allow segmentation accuracy to be described in anatomical and clinical context rather than reported as a single aggregate score. The dataset supports the development and validation of methods for lumen segmentation, plaque and perivascular quantification, and haemodynamic modelling.

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Medical Image Localization

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ImageCAS-X: a dataset and benchmark for coronary artery segmentation and centerline extraction in coronary CT angiography

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Beyond Volume Overlap: Surface Matching for Topology-Aware Coronary Artery Segmentation