Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value Delta. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image's voxels. The weightings of the graph's terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.Comment: 23 figures, 2 tables, 43 references, PLoS ONE 9(4): e9338

Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods

Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the underexplored task of translating saliency maps into natural language and compare methods that address two key challenges of this approach -- what and how to verbalize. In both automatic and human evaluation setups, using token-level attributions from text classification tasks, we compare two novel methods (search-based and instruction-based verbalizations) against conventional feature importance representations (heatmap visualizations and extractive rationales), measuring simulatability, faithfulness, helpfulness and ease of understanding. Instructing GPT-3.5 to generate saliency map verbalizations yields plausible explanations which include associations, abstractive summarization and commonsense reasoning, achieving by far the highest human ratings, but they are not faithfully capturing numeric information and are inconsistent in their interpretation of the task. In comparison, our search-based, model-free verbalization approach efficiently completes templated verbalizations, is faithful by design, but falls short in helpfulness and simulatability. Our results suggest that saliency map verbalization makes feature attribution explanations more comprehensible and less cognitively challenging to humans than conventional representations.Comment: ACL 2023 Workshop on Natural Language Reasoning and Structured Explanations (NLRSE

Detection of Beat Cepheids in M33 and Their Use as a Probe of the M33 Metallicity Distribution

Our analysis of the Deep CFHT M33 variability survey database has uncovered 5 Beat Cepheids (BCs) that are pulsating in the fundamental and first overtone modes. With {\it only} the help of stellar pulsation theory and of mass--luminosity (M-L) relations, derived from evolutionary tracks, we can accurately determine the metallicities Z of these stars. The [O/H] metallicity gradient of -0.16 dex/kpc that is inferred from the M33 galacto-centric distances of these Cepheids and from their 'pulsation' metallicities is in excellent agreement with the standard spectroscopic metallicity gradients that are determined from H II regions, early B supergiant stars and planetary nebulae. Beat Cepheids can thus provide an additional, independent probe of galactic metallicity distributions.Comment: 5 pages, 2 fig