The researchers call their architecture ResNet50-MASAG-UNet, and each component of that name encodes a deliberate technical choice. The backbone is a UNet, the encoder-decoder network that has dominated biomedical segmentation since 2015, but with its standard encoder replaced by ResNet50, a 50-layer residual network pretrained on ImageNet. That transfer learning decision is crucial because the team evaluated their system on the DCA1 database, a public collection of just 134 grayscale angiograms of 300 by 300 pixels, each annotated with ground-truth vessel masks prepared by expert cardiologists at the Cardiology Department of the Mexican Social Security Institute. Small datasets like this cause deep encoders trained from scratch to overfit badly; the ablation numbers show it plainly. With random initialization the model reaches a Dice coefficient of 81.2 percent, but with ImageNet-pretrained weights it climbs to 88.3 percent—a gain of more than seven points attributable to pretraining alone.

Before any network sees the images, the pipeline applies a three-stage preprocessing sequence designed to rescue vessel visibility from the raw angiogram’s low signal-to-noise ratio. First, bilateral filtering with a neighbourhood diameter of 9 and color and spatial sigma values of 75 smooths background noise while preserving vessel edges, because bilateral filtering weights neighbours by both spatial distance and intensity similarity. Second, unsharp masking with a 9 by 9 Gaussian kernel, sigma of 10.0, and an amount factor of 1.5 sharpens fine vascular details by subtracting a blurred copy of the image from the original. Third, Contrast Limited Adaptive Histogram Equalization with a clip limit of 3.0 on an 8 by 8 tile grid redistributes local intensity contrast and corrects uneven brightness without amplifying noise in dark regions. The order matters: noise reduction comes first, edge sharpening second, local contrast enhancement last, so that CLAHE does not magnify artefacts the earlier steps have not yet suppressed. The preprocessing ablation is striking—raw images yield 80.2 percent Dice, the full pipeline 88.3 percent, and the boundary error metric HD95 falls from 12.6 pixels to 4.1 pixels.

The genuinely novel element sits at the skip connections, the channels through which a UNet passes encoder features directly to the decoder. In a standard UNet these connections are a liability in angiography: they carry catheter outlines, bone edges, and heart-shadow noise straight into the reconstruction path, weakening the recovery of thin vessels. The team replaced them with a Multi-Scale Adaptive Spatial Attention Gate, or MASAG, adapted from the MSA2Net architecture of Kolahi and colleagues and installed at all four encoder skip levels, with channel dimensions of 64, 64, 128, and 256. Each MASAG operates in four stages. It first extracts multi-scale features from the encoder signal using a depth-wise separable 3 by 3 convolution for fine vessel detail and a dilated convolution with rate 2 for wider structures such as curves and branches, fusing them with global decoder context aligned through global average pooling and upsampling. A Softmax-based spatial selection map then generates two attention weights that reweight the encoder and decoder features, allowing the two paths to compete per pixel for influence.

What follows is the two-way interaction that distinguishes MASAG from earlier attention modules like CBAM or the attention gates of Schlemper and colleagues. The reweighted encoder and decoder features cross-modulate each other—the encoder signal modulates the decoder guidance and vice versa—producing an interaction feature that captures joint vessel-relevant information from both directions. Finally, a channel attention vector computed with a reduction ratio of 8 recalibrates the output before it reaches the decoder. The ablation table makes the case quantitatively: within the identical ResNet50-UNet backbone, an attention gate achieves 85.4 percent Dice, CBAM 86.6 percent, and MASAG 88.3 percent. Removing MASAG entirely drops performance by roughly 8 to 10 Dice points, and equipping all four skip connections outperforms one, two, or three, with the four-skip configuration reaching the full 88.3 percent. Image-level paired statistical tests across all 134 out-of-fold predictions confirm the gains are significant, with Cohen’s d effect sizes of 1.55 or larger and p-values below 0.001 after Bonferroni correction.

Attention alone, however, cannot guarantee that the predicted vessel tree remains anatomically connected. Binary cross-entropy and Dice losses optimize pixel overlap and region similarity, but a prediction can score well on both while fragmenting the centreline into disconnected pieces—a fatal flaw for any downstream stenosis analysis that depends on tracing a continuous path along an artery. The team therefore trained with a hybrid loss combining three terms in equal weights: binary cross-entropy for pixel-level refinement, Dice for region overlap and class imbalance, and centreline Dice, or clDice, for topological connectivity. clDice, introduced by Shit and colleagues in 2021, compares the centreline structures of prediction and ground truth using a differentiable soft-skeletonization operation in which iterative min and max pooling with a 3 by 3 kernel approximate morphological erosion and dilation. Five iterations suffice to extract thin coronary skeletons at the 256 by 256 working resolution. When a vessel breaks, the skeleton fragments, both centreline-matching terms fall, and the loss rises—forcing the network to learn connectivity as a training objective rather than hoping for it as a by-product.

The topology-aware loss earns its place in the numbers. Adding clDice to a Dice-plus-BCE baseline lifts Dice from 85.9 to 88.3 percent, the clDice metric itself from 86.3 to 89.1 percent, HD95 from 7.4 to 4.1 pixels, and the Betti-0 error—a count of connected components in the prediction versus the ground truth, using 8-connectivity appropriate for thin diagonally oriented vessels—from 3.1 to 1.5. A sensitivity analysis varying the clDice loss weight from 0.1 to 5.0 shows performance stable across the range, peaking at the balanced equal-weight setting, so the choice is not a fragile hyperparameter. The team also reports the 95th percentile Hausdorff distance to capture boundary-level accuracy and penalize large local deviations, and they candidly note a limitation of Betti-0 itself: because it compares only component counts, a false merge and a false split can offset each other and leave the count unchanged, so they interpret it alongside overlap and boundary metrics rather than in isolation.

Training followed a two-phase schedule on an NVIDIA Tesla P100 GPU in the Kaggle environment using Keras-TensorFlow with Python 3.10. For the first 25 epochs the ResNet50 encoder stayed frozen while the MASAG gates and decoder learned stable features at a learning rate of 1 times 10 to the minus 4; the encoder was then fully unfrozen for another 25 epochs of joint fine-tuning, a strategy the ablation confirms outperforms both a permanently frozen encoder at 84.1 percent Dice and selective unfreezing of deeper stages at 86.5 percent. The complete model carries 37,397,633 parameters, requires roughly 32.5 GFLOPs per forward pass at 256 by 256 input, trains in about four minutes per fold, and infers in approximately 22 milliseconds per image—fast enough, in principle, for clinical workflows. Five-fold cross-validation, with four folds splitting 107 training and 27 validation images and one fold 108 and 26, produced the reported means and standard deviations, and the learning curves show training and validation losses declining in close alignment from roughly 0.90 to 0.01 and 0.88 to 0.02 respectively, with narrow shaded bands across folds indicating stable generalization without major overfitting.

The headline comparison is blunt: the standard UNet baseline reaches 74.0 percent Dice, UNet++ 74.7 percent, and a ResNet50-UNet without attention 76.1 percent, while the full proposed model attains 88.3 percent Dice, 79.1 percent Jaccard, 86.4 percent precision, and 90.5 percent recall. Against recently reported methods on DCA1—including FiTNet at 81.1 percent, EAG-SAE-ESTF at 80.6 percent, and CoorMa at 80.4 percent—the proposed model reports among the highest Dice, Jaccard, and recall values, though the authors are careful to state that these studies used different preprocessing and evaluation protocols, so the comparison is a reference rather than a strictly controlled head-to-head benchmark. Qualitative results, with true positives in green, false positives in red, and false negatives in blue, show the model maintaining a consistent balance between error types and suppressing non-vascular activations from catheters and bones without any separate removal step.

The authors are equally candid about the boundaries of the achievement. The evaluation rests on a single public dataset of 134 images with cross-validation folds rather than a separate test set, so performance may appear slightly better than it actually is, and patient-level independence between training and validation splits cannot be verified because the distributed images carry no patient identifiers. Thin distal vessel branches are sometimes missed, very low-contrast segments can blend into surrounding tissue, and unsharp masking and CLAHE may amplify background noise in some areas despite bilateral filtering and a moderate clip limit. Most tellingly, a zero-shot test on the external XCAD dataset of 126 images from a different imaging system—resized but not preprocessed—yielded a Dice of just 22.3 percent, a sobering measure of the domain gap between imaging systems and a reminder that cross-dataset adaptation remains future work. No clinical tasks such as stenosis quantification were evaluated, and the authors note that expert-annotated stenosis data, which DCA1 does not provide, would be required for blockage percentage analysis. Still, the structural basis is now in place: a segmentation that keeps the coronary tree connected, suppresses the visual clutter of the catheter lab, and runs in milliseconds. Whether that foundation survives contact with diverse hospital data from different scanners is the question the next phase of this research must answer.

Subject of Research: Artificial Intelligence

Article Title: Topology-preserving coronary vessel segmentation in X-ray angiography using ResNet50-MASAG-UNet

Article References: Sunilkumar, G., & Kumaresan, P. (2026). Topology-preserving coronary vessel segmentation in X-ray angiography using ResNet50-MASAG-UNet. Frontiers in Radiology, 6, Article 1949450. https://doi.org/10.3389/fradi.2026.1949450

Image Credits: AI Generated

DOI: 10.3389/fradi.2026.1949450

Keywords: coronary artery disease, X-ray coronary angiography, deep learning, vessel segmentation, attention gates, topology-preserving loss, ResNet50, medical imaging, Topology-preserving, coronary, vessel, segmentation