Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

A review on the recent segmentation and tumor grade classification techniques of brain
Magnetic Resonance (MR) Images is the objective of this paper. The requisite for early …

Obtaining quantitative measures from biomedical images often requires segmentation, ie,
finding and outlining the structures of interest. Multi-modality imaging datasets, in which …

Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume
changes from computed tomography images are essential for monitoring tumor response to …

MRI-based medical image analysis for brain tumor studies is gaining attention in recent
times due to an increased need for efficient and objective evaluation of large amounts of …

Due to advances in the acquisition and analysis of medical imaging, it is currently possible
to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by …

Purpose The aim of this study is to determine if radiomics features from 18
fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) …

Abstract Objectives At a European Society of Neuroradiology (ESNR) Annual Meeting 2015
workshop, commonalities in practice, current controversies and technical hurdles in glioma …

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D
medical images. This paper summarizes major new features added to ITK-SNAP over the …

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for
adequately informing treatments. In this study we assessed the clinical relevance of a …