C. Karabag

Evaluation of training data, ground truth and shape variability in the semantic segmentation of HeLa cells observed with electron microscopy

The paper evaluates training data, ground truth, and shape variability in HeLa cell segmentation using electron microscopy, finding that larger training sets improve accuracy but single-cell models struggle with complex shapes.

Volumetric Semantic Instance Segmentation of the Plasma Membrane of HeLa Cells

This paper describes an unsupervised method for volumetric semantic segmentation of HeLa cell plasma membranes using serial block face scanning electron microscopy data.

Estimation of cellularity in tumours treated with Neoadjuvant therapy: A comparison of Machine Learning algorithms

This paper compares machine learning algorithms for estimating residual tumour cellularity in breast cancer treated with neoadjuvant therapy, finding Support Vector Machines to be the most accurate.

Geometric differences between nuclear envelopes of Wild-type and Chlamydia trachomatis-infected HeLa cells

The paper compares geometric features of nuclear envelopes in wild-type and Chlamydia trachomatis-infected HeLa cells using electron microscopy and spheroid modeling.

Morphological estimation of Cellularity on Neo-adjuvant treated breast cancer histological images

The paper estimates cellularity in neo-adjuvant treated breast cancer histological images using morphological features and finds strong correlations between specific parameters and residual tumour cellularity.

Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures

This paper compares a traditional image-processing algorithm with three deep learning models for semantic segmentation of HeLa cells, finding the traditional method outperforms all deep learning architectures.