Papers for

medical imaging software engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Controlling variability in MRI analysis pipelines with software principles

Controlling Variability in~Neuroimaging Analysis Pipelines with~Software Product Lines

Abstract: Magnetic resonance imaging (MRI) is an acquisition technique used for medical diagnosis. It is notably used for structural and functional MRI (or fMRI) image analysis. MRI data is known to be complex and noisy, so pipelines are created to extract and enhance relevant information in retrieved images. The NARPS study highlighted the significant variability that can emerge from different pipelines set up by independent teams while trying to answer the same research question about brain functioning. Having different implementations realizing the same task is a typical playground for software product lines (SPLs), which may be out of the sight of neuroimage practitioners. Thus, proposing an SPL out of these different pipelines falls back on a typical reverse-engineering problem with its usual difficulties. In this document, we advocate for the conceptual adoption of SPL principles as a promising direction to improve reproducibility and consistency in neuroimaging research.

Wed 21 OctSoftware Engineering
The gist
MRI scans are complex and can produce noisy data, so scientists use processing steps called pipelines to make sense of them. Different teams often create their own pipelines, which can result in big differences even when studying the same brain data. The authors suggest using software product line (SPL) methods, which help manage different software versions, to organize and standardize these pipelines. This approach could make brain imaging studies more reliable and easier to compare.
Open → 2610.29821v1

Spatiotemporal model improves needle tip detection in ultrasound videos

STUNet-Fusion: Spatiotemporal Needle-Tip Localization in Ultrasound Video via Multi-Channel Motion Fusion

Abstract: Needle-tip localization in ultrasound remains challenging because the needle may appear weak, discontinuous, or partially invisible, while imaging artifacts and anatomical structures can produce similar responses. To address this problem, we propose STUNet-Fusion, a spatiotemporal framework for needle-tip localization in ultrasound videos. The proposed method formulates the input as a tri-channel spatio-temporal fusion tensor, comprising grayscale appearance, grid-based motion feature, and raw frame difference. A shared ResNet-34 encoder extracts spatial features, ConvLSTM integrates temporal dependencies, and a U-Net decoder reconstructs a dense probability heatmap. The final coordinates are extracted via a soft-argmax operation to achieve sub-pixel localization accuracy. Experimental results demonstrate that this spatiotemporal fusion strategy significantly improves localization robustness compared to conventional baselines.

Wed 16 SeptComputer Vision and Pattern Recognition
The gist
Finding the needle tip in ultrasound images is hard because it can look faint or be blocked, and other things in the image can look similar. The authors created a method called STUNet-Fusion that combines information about how the picture looks, how things move, and changes between frames over time. Their system uses a neural network to learn both the shape and movement patterns, then predicts exactly where the needle tip is in each video frame. Tests show this approach is more reliable than older methods.
Open → 2609.18546v1