arXiv subjects

Víctor M. González

Publications and source records attributed to Víctor M. González.

7 recordsLinked to original sources

arXiv2026

Automatic Generation of Expert-Level Neuron Segmentation Masks from Fluorescence Microscopy Images for Non-Invasive Deep Learning Analysis of Phase-Contrast Images

Background & Objective Accurate segmentation of neurons in microscopy images of neuronal cultures is crucial for research on neurodegenerative diseases and neurotoxicity. Manual annotation of such images is time-consuming, subjective, and inconsistent across experts. Deep learning (DL) models offer an effective alternative, but require high-quality training datasets composed of microscopy images with accurately segmented neurons, typically created by experts. Neuronal cultures can be imaged using either phase-contrast or fluorescence microscopy. While live neurons are easily detected in fluorescence images, this modality requires staining, which affects cell viability. Conversely, phase-contrast imaging is non-invasive but makes neuron identification more challenging. Methods In this work we present a robust computer vision pipeline for the automatic generation of expert-level neuron segmentation masks from fluorescence microscopy images. These masks are used to train DL models for neuron identification in phase-contrast images. Our method relies on paired fluorescence and phase-contrast images captured simultaneously at the same magnification and coordinates, ensuring alignment across modalities. Once trained, the DL models can segment neurons in phase-contrast images alone, eliminating the need for fluorescence staining. Results We validate our approach by comparing the automatically generated masks with expert annotations using multiple agreement metrics, including IoU, Accuracy, and Gwet's AC1. Such metrics are calculated not only for the identification but also for the segmentation of instances. Conclusions This study presents a classical computer vision algorithm for generating neuron segmentation masks, which can be used for downstream applications such as non-invasive cell viability analyses by means of DL.

q-bio.NC↗

arXiv2026

Neoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables

Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer patients. This work proposes a deep-learning model for pretreatment data only, combining apparent diffusion coefficient maps, dynamic contrast-enhanced magnetic resonance imaging, and clinical variables. The study uses the public ACRIN 6698/I-SPY2 multicenter dataset. The architecture employs EfficientNet-B0 pretrained encoders for image feature extraction and late fusion with clinical information. Multiple clinical variables were evaluated, including age, race, histological type, HR/HER2 subtype, SBR grade, and maximum diameter. Only HR/HER2 subtype improved the average area under the receiver operating characteristic curve (AUC) and was retained in the final model. Using stratified five-fold cross-validation, standalone apparent diffusion coefficient maps achieved a mean AUC of 0.79, whereas dynamic contrast-enhanced magnetic resonance imaging achieved 0.74. Adding HR/HER2 subtype improved performance to 0.83 and 0.81, respectively. The final configuration, using both imaging modalities and HR/HER2 subtype, achieved an AUC of 0.86. These results support pretreatment multimodal learning for response prediction, although external validation is required before clinical use.

cs.AI↗

arXiv2026

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Temporal Deep Learning on DWI

Early identification of non-responders to neoadjuvant chemotherapy (NACT) is crucial for timely treatment adaptation in breast cancer. However, many existing predictive models rely on multiparametric magnetic resonance imaging (MRI), late treatment time points, or extensive clinical data, which limits their applicability. This study proposes a deep learning framework for early prediction of pathological complete response (pCR) using only diffusion-weighted MRI (DW-MRI) acquired at baseline and after the first NACT cycle. This framework feeds cropped tumor-centered patches to an EfficientNet-based temporal model that directly learns tumor shape and local tissue characteristics without explicit radiomic feature engineering. The model, trained with 10-fold cross-validation, achieved an area under the receiver operating characteristic curve (AUC) of 0.90 for pCR prediction after one cycle, providing actionable information after a single treatment cycle while avoiding gadolinium administration and reducing dependence on heterogeneous clinical data. By focusing on the baseline-to-first-cycle window instead of later stages, the approach supports earlier escalation or de-escalation of NACT, and its exclusive reliance on DW-MRI facilitates protocol standardization, multi-centre deployment and privacy-preserving data sharing. These results demonstrate that DW-MRI-based deep learning on tumor-centered patches constitutes a minimally invasive, clinically deployable strategy for early pCR prediction, with direct implications for personalized treatment adaptation in neoadjuvant breast cancer therapy.

cs.AI↗

arXiv2026

Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI

Dynamic contrast-enhanced (DCE) imaging is the gold standard technique for the detection and characterization of breast cancer using magnetic resonance imaging (MRI). However, DCE-MRI requires long acquisition times and the administration of contrast into the bloodstream, which can cause allergic reactions. Alternatively, diffusion-weighted MRI (DW-MRI) is a standard complementary technique for breast MRI that does not require contrast, has shorter acquisition times, and enables calculation of apparent diffusion coefficient (ADC) maps that correlate with tumor cellularity. Yet, despite these technical advantages, deep learning research has focused on DCE-based models and has barely explored the tumor detection performance of DW-MRI and ADC maps either in combination with DCE-MRI or as standalone alternatives. Here, we evaluate the application of different state-of-the-art deep learning techniques for detection and segmentation of breast cancer using ADC-only images. This is, to our knowledge, the first comprehensive evaluation of ADC-only breast cancer pipelines for classification, detection, and segmentation tasks.

cs.CV↗

arXiv2026

NeuroAdaptTrainer: A Fiji/ImageJ Plugin for YOLO-Based Neuron Segmentation, InteractiveCorrection and Transfer Learning

Neuron counting and segmentation in microscopy images of neuronal cultures is a routine and time-consuming task in neuroscience research, traditionally performed through manual inspection or semi-automatic tools. We present NeuroAdaptTrainer, an open-source Fiji/ImageJ plugin that integrates a YOLO instance-segmentation model directly into the microscopist's workflow. The plugin allows a user to run automatic neuron detection on a single image or a batch of images, manually correct the resulting detections from within Fiji, and use those corrections to adapt the model to new imaging conditions via transfer learning. A built-in external validation module allows the base and adapted models to be compared quantitatively on a held-out annotated set. NeuroAdaptTrainer lowers the barrier for non-specialist users to benefit from deep-learning-based segmentation while keeping expert supervision at the center of the workflow.

cs.CV↗

arXiv2026

VR-IPS: Virtual Reality Tool for Photic Stimulation in Photosensitive Diagnosis

Photosensitivity is a neurological condition in which the brain generates epileptiform activity in response to visual stimuli. The standardized clinical diagnosis procedure, named Intermitent Photic Stimulation (IPS), involves exposing patients to a white flashing light at different frequencies to provoke this reaction. However, clinical neurophysiologists report that this protocol is insufficient, leading to underdiagnosis. VR-IPS is a flexible visual stimulation tool that extends conventional flashing-light stimulation by incorporating configurable color lights, static and oscillatory motion patterns, and photoprovocative videos. The system comprises a stimulation module that can run on Virtual Reality headsets or on standard monitors, together with a control module that allows clinicians to create, select, and configure visual stimulus, while controlling the whole stimulation procedure.

q-bio.NC↗

arXiv2025

Inception networks, Data Augmentation and Transfer Learning in EEG-based photosensitivity diagnosis

Photosensitivity refers to a neurophysiological condition in which the brain generates epileptic discharges known as Photoparoxysmal Responses (PPR) in response to light flashes.In severe cases, these PPR can lead to epileptic seizures. The standardized diagnostic procedure for this condition is called Intermittent Photic Stimulation. During this procedure, the patient is exposed to a flashing light, aiming to trigger these epileptic reactions while preventing their full development. Meanwhile, brain activity is monitored using Electroencephalography, which is visually analyzed by clinical staff to identify these responses. Hence, the automatic detection of PPR becomes a highly unbalanced problem that has been barely studied in the literature due to photosensitivity's low prevalence. This research tackles this problem and proposes using Inception-based Deep Learning (DL) neural networks that, together with transfer learning, are trained in epilepsy seizure detection and tuned in the PPR automatic detection task. A Data Augmentation (DA) technique is also applied to balance the available data set, evaluating its effects on the DL models. The proposal outperformed state-of-the-art solutions in the literature, achieving higher ratios on standard performance metrics, and with DA significantly improving the Sensitivity without affecting Accuracy and Specificity. This project is currently being developed with patients from Burgos University Hospital, Spain

eess.SP↗