Automatic liver tumor segmentation in follow-up CT studies using Convolutional Neural Networks

Abstract We present a new, fully automatic algorithm for liver tumors seg-mentation in follow-up CT studies. The inputs are a baseline CT scan and a de-lineation of the tumors in it and a follow-up scan; the outputs are the tumors…

Tumor burden evaluation in NF1 patients with plexiform neurofibromas in daily clinical practice

Abstract Background Existing volumetric measurements of plexiform neurofibromas (PNs) are time consuming and error prone, as they require delineation of PN boundaries, a procedure that is not practical in the typical clinical setting. The aim of this study is to…

Automatic lung tumor segmentation with leaks removal in follow-up CT studies

Abstract Purpose In modern oncology, disease progression and response to treatment are routinely evaluated with a series of volumetric scans. The number of tumors and their volume (mass) over time provides a quantitative measure for the evaluation. Thus, many of…

The Effect of Chemotherapy on Optic Pathway Gliomas and Their Sub-Components: A Volumetric MR Analysis Study

Abstract Background. Optic pathway gliomas (OPG) represent 5% of pediatric brain tumors and compose a major therapeutic dilemma to the treating physicians. While chemotherapy is widely used for these tumors, our ability to predict radiological response is still lacking. In…

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

Abstract Purpose: Tracking the progression of low grade tumors (LGTs) is a challenging task, due to their slow growth rate and associated complex internal tumor components, such as heterogeneous enhancement, hemorrhage, and cysts. In this paper, the authors show a…

Automatic lung tumor segmentation with leaks removal in follow-up CT studies

Abstract We present a novel automatic algorithm for lung tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it; the output is the tumor delineations in the follow-up scan.…

Automatic methods for tumor segmentation and follow-up in MR images

Abstract Magnetic resonance imaging (MRI) is the method of choice for the noninvasive assessment of tumors. The common treatment evaluation of tumors is based on MRI scans acquired every several weeks or months. It is crucial to accurately quantify the…

Quantitative functional MRI Biomarkers Improved Early Detection of Colorectal Liver Metastases

Abstract Purpose: To implement and evaluate the performance of a computerized statistical tool designed for robust and quantitative analysis of hemodynamic response imaging (HRI) -derived maps for the early identification of colorectal liver metastases (CRLM). Materials and Methods: CRLM-bearing mice…

Insights into Volumetric and Sub Segmentation Long-term Analysis of Treated OPG Patients Receiving Chemotherapy

Abstract INTRODUCTION: Optic pathway gliomas pose a major management difficulty in NF1 and sporadic patients. Diagnosis and follow up are heavily based on multiple MR imaging. Currently, assessment of tumor volume and response to treatment is based on single slice…

Fast Semi-Automatic Plexiform Neurofibroma Tumor Segmentation in MRI Scans

Abstract Neurofibromatosis-1 (NF-1) is a common genetic disorder associated with the frequent occurrence of central and peripheral nervous system tumors. Plexiform Neurofibromas (PN) are one of the hallmarks of NF-1. These are benign nerve sheath tumors with a complex shape,…

MRI internal segmentation of optic pathway gliomas: clinical implementation of a novel algorithm

Abstract Purpose Optic pathway gliomas (OPGs) are diagnosed based on typical MR features and require careful monitoring with serial MRI. Reliable, serial radiological comparison of OPGs is a difficult task, where accuracy becomes very important for clinical decisions on treatment…

Automatic segmentation, internal classification, and follow-up of optic pathway gliomas in MRI

Abstract This paper presents an automatic method for the segmentation, internal classification and follow-up of optic pathway gliomas (OPGs) from multi-sequence MRI datasets. Our method starts with the automatic localization of the OPG and its core with an anatomical atlas…

Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation

Abstract Objective We present a method and a validation study for the nearly automatic segmentation of liver tumors in CTA scans. Materials and methods Our method inputs a liver CTA scan and a small number of user-defined seeds. It first…

Longitudinal Assessment of Brain Tumors Using a Repeatable Prior-based Segmentation

Abstract This paper presents an automatic method for a repeatable, prior-based segmentation and classification of brain tumors in longitudinal MR scans. The method is designed to overcome the inter/intra observer variability and to provide a repeatable delineation of the tumor…

Automatic Segmentation and Components Classification of Optic Pathway Gliomas in MRI

Abstract We present a new method for the automatic segmentation and components classification of brain Optic Pathway Gliomas (OPGs) from multi-spectral MRI datasets. Our method accurately identifies the sharp OPG boundaries and consistently delineates the missing contours by effectively incorporating…

Liver Tumor Segmentation and Volume Computation with User-Guided 3D Active Contours

Abstract Tumor volume computation is a crucial task routinely performed in radiology departments in order to assess the effectiveness of treatments. Today, radiologists perform this task by estimating the tumor volume using a set of simple guidelines. To improve this…

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