Repurposing traditional U-Net predictions for sparse SAM prompting in medical image segmentation.

Objective:Automated medical image segmentation (MIS) using deep learning has traditionally relied on models built and trained from scratch, or at least fine-tuned on a target dataset. The Segment Anything Model (SAM) by Meta challenges this paradigm by providing zero-shot generalisation capabilities. This study aims to develop and compare methods for refining traditional U-Net segmentations by repurposing them for automated SAM prompting.Approach:A 2D U-Net with EfficientNet-B4 encoder was trained using 4-fold cross-validation on an in-house brain metastases dataset. Segmentation predictions from each validation set were used for automatic sparse prompt generation via a bounding box prompting method (BBPM) and novel implementations of the point prompting method (PPM). The PPMs frequently produced poor slice predictions (PSPs) that required identification and substitution. A slice was identified as a PSP if it (1) contained multiple predicted regions per lesion or (2) possessed outlier foreground pixel counts relative to the patient's other slices. Each PSP was substituted with a corresponding initial U-Net or SAM BBPM prediction. The patients' mean volumetric dice similarity coefficient (DSC) was used to evaluate and compare the methods' performances.Main results:Relative to the initial U-Net segmentations, the BBPM improved mean patient DSC by 3.93 ± 1.48% to 0.847 ± 0.008 DSC. PSPs constituted 20.01-21.63% of PPMs' predictions and without substitution performance dropped by 82.94 ± 3.17% to 0.139 ± 0.023 DSC. Pairing the two PSP identification techniques yielded a sensitivity to PSPs of 92.95 ± 1.20%. By combining this approach with BBPM prediction substitution, the PPMs achieved segmentation accuracies on par with the BBPM, improving mean patient DSC by up to 4.17 ± 1.40% and reaching 0.849 ± 0.007 DSC.Significance:The proposed PSP identification and substitution techniques bridge the gap between PPM and BBPM performance for MIS. Additionally, the uniformity observed in our experiments' results demonstrates the robustness of SAM to variations in prompting style. These findings can assist in the design of both automatically and manually prompted pipelines.

Curatr: a web application for creating, curating and sharing a mass spectral library.

Summary: We have developed a web application curatr for the rapid generation of high quality mass spectral fragmentation libraries from liquid-chromatography mass spectrometry datasets. Curatr handles datasets from single or multiplexed standards and extracts chromatographic profiles and potential fragmentation spectra for multiple adducts. An intuitive interface helps users to select high quality spectra that are stored along with searchable molecular information, the providence of each standard and experimental metadata. Curatr supports exports to several standard formats for use with third party software or submission to repositories. We demonstrate the use of curatr to generate the EMBL Metabolomics Core Facility spectral library http://curatr.mcf.embl.de. Availability and implementation: Source code and example data are at http://github.com/alexandrovteam/curatr/. Contact: palmer@embl.de. Supplementary information: Supplementary data are available at Bioinformatics online.

FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.

High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics.