arXiv subjects

Soumen Ghosh

Publications and source records attributed to Soumen Ghosh.

8 recordsLinked to original sources

arXiv2026

CRIL-U-Net: Compact Ratio-Interaction Learning for Focal Cortical Dysplasia Segmentation from T1w and FLAIR MRI

Focal cortical dysplasia (FCD) type II is an important structural cause of drug-resistant focal epilepsy, but its small size, heterogeneous appearance, and subtle MRI characteristics make automated segmentation challenging. Conventional multimodal networks commonly concatenate T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images, requiring subsequent layers to learn useful cross-modal relationships implicitly. We propose CRIL-U-Net, a 3D U-Net incorporating a Compact Ratio-Interaction Learning module that combines local spatial features, voxel-wise cross-modal mixing, and bidirectional ratio-inspired interactions. CRIL-U-Net was compared with a conventional 3D U-Net and an input self-attention U-Net using five-fold cross-validation on 85 FCD subjects and 25 healthy controls. Each architecture was trained independently using Dice-binary cross-entropy (Dice-BCE) and Focal Tversky-Focal (FTF) losses. With FTF, CRIL-U-Net achieved the highest mean Dice score (0.196 +/- 0.262), compared with 0.136 +/- 0.224 for the U-Net and 0.135 +/- 0.214 for the attention comparator. It produced nonzero lesion overlap in 44 of 85 cases, compared with 36 for the U-Net. Under FTF, CRIL-U-Net significantly outperformed both comparison architectures after false-discovery-rate correction. These findings suggest that compact cross-modal representation learning can improve FCD segmentation within a controlled U-Net setting when combined with an imbalance-aware objective, although the remaining zero-overlap rate of 48.2% highlights the need for further validation and methodological development.

cs.CV↗

arXiv2026

Benchmarking MRI Representations for Deep Learning-Based Focal Cortical Dysplasia Segmentation

Focal cortical dysplasia (FCD) is one of the leading structural causes of drug-resistant focal epilepsy, yet its subtle and heterogeneous imaging characteristics make accurate identification and delineation challenging on conventional magnetic resonance imaging (MRI). Although T1-weighted (T1w) and fluid-attenuated inversion recovery (FLAIR) images are routinely acquired for presurgical evaluation, the contribution of different MRI representations to deep learning-based FCD segmentation remains poorly understood. In this study, we present a systematic benchmark of MRI representations for automated FCD segmentation using the nnU-Net framework. A publicly available presurgical MRI dataset comprising 85 FCD subjects and 25 healthy controls was used to evaluate eight input configurations, including conventional MRI contrasts (T1w and FLAIR), ratio-derived representations, and their multimodal combinations. To isolate the effect of MRI representation, all experiments employed identical preprocessing, network architecture, optimization strategy, and five-fold cross-validation. Among the evaluated single-modality representations, FLAIR achieved the strongest overall performance, whereas ratio-derived representations alone were insufficient for reliable identification of subtle FCD. Incorporating ratio-derived representations with conventional T1w and FLAIR images consistently improved lesion delineation, with the four-channel multimodal configuration achieving the highest overall Dice score (0.376), representing a 5.0% relative improvement over the conventional T1w+FLAIR representation. These findings demonstrate that MRI representation design is an important yet underexplored component of deep learning-based FCD segmentation and should be optimized alongside network architecture.

cs.CV↗

arXiv2025

Tracking Electron, Proton, and Solvent Motion in Proton-Coupled Electron Transfer with Ultrafast X-rays

Proton-coupled electron transfer (PCET) is foundational to catalysis, bioenergetics, and energy conversion, yet capturing and disentangling the coupled motions of electrons, protons, and solvent has remained a major experimental challenge. We combine femtosecond optical spectroscopy, site-specific ultrafast soft X-ray absorption spectroscopy, and time-resolved X-ray scattering with advanced calculations to disentangle the elementary steps of PCET in solution. Using a ruthenium polypyridyl model complex, we directly resolve photoinduced electron redistribution, ligand-site protonation within 100 ps, and the accompanying solvent reorganization. This unified multi-modal approach provides an orbital-level, atomistic picture of PCET, showing how electronic, nuclear, and solvation degrees of freedom can be separated experimentally. Our results establish a general X-ray framework for understanding and ultimately controlling PCET in catalysis, artificial photosynthesis, and biological energy flow.

physics.chem-ph↗

arXiv2025

Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets

Robust generalization is essential for deploying deep learning based tumor segmentation in clinical PET-CT workflows, where anatomical sites, scanners, and patient populations vary widely. This study presents the first cross cancer evaluation of nnU-Net on PET-CT, introducing two novel, expert-annotated whole-body datasets. 279 patients with oesophageal cancer (Australian cohort) and 54 with lung cancer (Indian cohort). These cohorts complement the public AutoPET dataset and enable systematic stress-testing of cross domain performance. We trained and tested 3D nnUNet models under three paradigms. Target only (oesophageal), public only (AutoPET), and combined training. For the tested sets, the oesophageal only model achieved the best in-domain accuracy (mean DSC, 57.8) but failed on external Indian lung cohort (mean DSC less than 3.4), indicating severe overfitting. The public only model generalized more broadly (mean DSC, 63.5 on AutoPET, 51.6 on Indian lung cohort) but underperformed in oesophageal Australian cohort (mean DSC, 26.7). The combined approach provided the most balanced results (mean DSC, lung (52.9), oesophageal (40.7), AutoPET (60.9)), reducing boundary errors and improving robustness across all cohorts. These findings demonstrate that dataset diversity, particularly multi demographic, multi center and multi cancer integration, outweighs architectural novelty as the key driver of robust generalization. This work presents the demography based cross cancer deep learning segmentation evaluation and highlights dataset diversity, rather than model complexity, as the foundation for clinically robust segmentation.

eess.IV↗

arXiv2024

NeuroMorphix: A Novel Brain MRI Asymmetry-specific Feature Construction Approach For Seizure Recurrence Prediction

Seizure recurrence is an important concern after an initial unprovoked seizure; without drug treatment, it occurs within 2 years in 40-50% of cases. The decision to treat currently relies on predictors of seizure recurrence risk that are inaccurate, resulting in unnecessary, possibly harmful, treatment in some patients and potentially preventable seizures in others. Because of the link between brain lesions and seizure recurrence, we developed a recurrence prediction tool using machine learning and clinical 3T brain MRI. We developed NeuroMorphix, a feature construction approach based on MRI brain anatomy. Each of seven NeuroMorphix features measures the absolute or relative difference between corresponding regions in each cerebral hemisphere. FreeSurfer was used to segment brain regions and to generate values for morphometric parameters (8 for each cortical region and 5 for each subcortical region). The parameters were then mapped to whole brain NeuroMorphix features, yielding a total of 91 features per subject. Features were generated for a first seizure patient cohort (n = 169) categorised into seizure recurrence and non-recurrence subgroups. State-of-the-art classification algorithms were trained and tested using NeuroMorphix features to predict seizure recurrence. Classification models using the top 5 features, ranked by sequential forward selection, demonstrated excellent performance in predicting seizure recurrence, with area under the ROC curve of 88-93%, accuracy of 83-89%, and F1 score of 83-90%. Highly ranked features aligned with structural alterations known to be associated with epilepsy. This study highlights the potential for targeted, data-driven approaches to aid clinical decision-making in brain disorders.

eess.IV↗

arXiv2019

RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively.

cs.CV↗

arXiv2019

Effect of Inhomogeneous magnetic field on Plasma generation in a low magnetic field helicon discharge

The ionization efficiency of helicon plasma discharge is explored by changing the low axial magnetic field gradients near the helicon antenna. The highest plasma density is found for a most possible diverging field near the antenna by keeping the other operating condition constant. Measurement of axial wave number together with estimated radial wavenumber suggests the oblique mode propagation of helicon wave along the resonance cone boundary. Propagation of helicon wave near the resonance cone angle boundary can excite electrostatic fluctuations which subsequently can deposit energy in the plasma. This process has been shown to be responsible for peaking in density in low field helicon discharges, where the helicon wave propagates at an angle with respect to the applied uniform magnetic field. The increased efficiency can be explained on the basis of multiple resonances for multimode excitation by the helicon antenna due to the availability of a broad range of magnetic field values in the near field of the antenna when a diverging magnetic field is applied in the source.

physics.plasm-ph↗

arXiv2018

Hollow density formation in magnetically expanding helicon plasma

Measurement of radial density profile in both the source and expansion chambers of a helicon plasma device have revealed that it is always centrally peaked in the source chamber, whereas in the expansion chamber near the diverging magnetic field it becomes hollow above a critical value of the magnetic field. This value corresponds to that above which both electrons and ions become magnetized. The temperature profile is always peaked off- axis and tail electrons are found at the peak location in both the source and expansion chambers. Rotation of the tail electrons in the azimuthal direction in the expansion chamber due to gradient-B drift produces more ionization off-axis and creates a hollow density profile; however, if the ions are not magnetized, the additional ionization does not cause hollowness.

physics.plasm-ph↗