Biology subjects

Ait-Ahmad, K.

Publications and source records attributed to Ait-Ahmad, K..

3 recordsLinked to original sources

bioRxiv2025

MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units

Cancer tissue analysis in digital pathology is typically conducted across different spatial scales, ranging from high-resolution cell-level modeling to lower-resolution tile-based assessments. However, these perspectives often overlook the structural organization of functional tissue units (FTUs), the small, repeating structures which are crucial to tissue function and key factors during pathological assessment. The incorporation of FTU information is hindered by the need for detailed manual annotations, which are costly and time-consuming to obtain. While artificial intelligence (AI)-based solutions hold great promise to accelerate this process, there is currently no comprehensive workflow for building the large, annotated cohorts required. To remove these roadblocks and advance the development of more interpretable approaches, we developed MiroSCOPE, an end-to-end AI-assisted platform for annotating FTUs at scale, built on QuPath. MiroSCOPE integrates a fine-tunable multiclass segmentation model and curation-specific usability features to enable a human-in-the-loop system that accelerates AI annotation by a pathologist. The system is used to efficiently annotate over 71,900 FTUs on 184 prostate cancer hematoxylin and eosin (H&E)-stained tissue samples and demonstrates ready translation to breast cancer. Furthermore, we publicly release a dataset named Miro-120, consisting of 120 prostate cancer H&E with 30,568 annotations, which can be used by the community as a high-quality resource for FTU-level machine learning aims. In summary, MiroSCOPE provides an adaptable AI-driven platform for annotating functional tissue units, facilitating the use of structural information in digital pathology analyses.

bioinformatics↗

bioRxiv2024

UniFORM: Towards Universal Immunofluorescence Normalization for Multiplex Tissue Imaging

Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including Z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline that uses an automated rigid landmark functional data registration approach for normalizing both feature- and pixel-level MTI data. Designed specifically for the distributional characteristics of MTI datasets, UniFORM operates without prior distributional assumptions and performs robustly regardless of distribution modality, including both unimodal and bimodal patterns. It removes technical variation by aligning the biologically invariant component of the signal, typically the negative (non-expressing) population, while preserving biologically meaningful variation in the positive population, thereby maintaining tissue-specific expression patterns essential for downstream analysis. Benchmarking across three distinct MTI platform datasets demonstrates that UniFORM outperforms existing methods in mitigating batch effects while maintaining biological signal fidelity. This is evidenced by improved marker distribution alignment and positive population preservation, enhanced kBET and Silhouette scores, and improved downstream analyses such as UMAP visualizations and Leiden clustering. UniFORM also introduces a novel guided fine-tuning option for complex and heterogeneous datasets. Although optimized for fluorescence-based platforms, UniFORM provides a scalable and robust solution for MTI data normalization, enabling accurate and biologically meaningful interpretations.

bioinformatics↗

bioRxiv2024

AxonFinder: Automated segmentation of tumor innervating neuronal fibers

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the diseases advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach. Utilizing a dataset of whole-slide images from low-, intermediate-, and high-risk prostate cancer patients, we manually annotated axons to train our model, achieving significant accuracy in detecting axonal structures that were previously hard to segment. Our analysis includes a comprehensive assessment of axon density and morphological features across different CAPRA-S prostate cancer risk categories, providing insights into the correlation between tumor innervation and cancer progression. Our paper suggests the potential utility of neuronal markers in the prognostic assessment of prostate cancer in aiding the pathologists assessment of tumor sections and advancing our understanding of neurosignaling in the tumor microenvironment.

systems biology↗