Florian Heyl | Searcharxiv

arXiv2026

The Challenge of Cell Segmentation in Spatially Resolved Transcriptomics

Spatially resolved transcriptomics (SRT) is transforming how we study tissues by measuring gene expression in cells in their spatial context. However, the field lacks robust methodological guidance on one of its most fundamental analytical steps: how to accurately segment cells and assign spatially localized transcripts to them. Major technical challenges include sparse molecular signals, transcript displacement, complex cellular morphologies, and the projection of three-dimensional tissue architecture onto two-dimensional imaging planes. These challenges make segmentation a major source of uncertainty, with errors that can propagate through downstream analyses and ultimately lead to misleading biological interpretations. Here, we argue that segmentation should be treated as a central unresolved problem in spatial omics rather than a routine preprocessing step. We review current approaches, highlight key methodological limitations, including the lack of appropriate metrics and gold-standard benchmarks, and propose a community-driven path forward. Establishing shared evaluation frameworks, scalable benchmark datasets, and transparent reporting standards will be essential for transforming SRT into a robust and reproducible foundation for biological discovery and clinical translation.

q-bio.OT↗

arXiv2017

TRIOT: Faster tensor manipulation in C++11

[abridged] Context: Multidimensional arrays are used by many different algorithms. As such, indexing and broadcasting complex operations over multidimensional arrays are ubiquitous tasks and can be performance limiting. Inquiry: Simultaneously indexing two or more multidimensional arrays with different shapes (e.g., copying data from one tensor to another larger, zero padded tensor in anticipation of a convolution) is difficult to do efficiently: Hard-coded nested for loops in C, Fortran, and Go cannot be applied when the dimension of a tensor is unknown at compile time. Likewise, boost::multi_array cannot be used unless the dimensions of the array are known at compile time, and the style of implementation restricts the user from using the index tuple inside a vectorized operation (as would be required to compute an expected value of a multidimensional distribution). On the other hand, iteration methods that do not require the dimensionality or shape to be known at compile time (e.g., incrementing and applying carry operations to index tuples or remapping integer indices in the flat array), can be substantially slower than hard-coded nested for loops. ... Importance: Manipulation of multidimensional arrays is a common task in software, especially in high performance numerical methods. This paper proposes a novel way to leverage template recursion to iterate over and apply operations to multidimensional arrays, and then demonstrates the superior performance and flexibility of operations that can be achieved using this new approach.

cs.MS↗