ArticleScientific reports2025
A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Automatic liver tumor segmentation using Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) provides detailed insights that facilitate accurate tumor targeting and effective treatment planning. However, challenges such as spill-out can distort tumor size, which complicates accurate segmentation. This study introduces a Paired Multi-Scale Attention Network (P-MANet) using Top-Hat Cross-Features to address these challenges by enhancing the identification of true positives while minimizing false positives in liver tumor segmentation. P-MANet employs a dual-branch architecture. The first branch utilizes a Multi-Scale Attention Network (MA-Net) to process SPECT/CT datasets, whereas the second branch applies the White Top-Hat Transform to extract features that are then integrated with those from the first branch. This innovative approach effectively mitigates errors stemming from spectral light variation, leading to improved accuracy in tumor delineation. The model was trained on a dataset comprising 43 cases of
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.