Vectorial multi-phase mouse brain tumor segmentation in T1-T2 MRI

Abstract

An automated, level-set based, segmentation framework is proposed in this work for computation of tumoral volumes on mice brain bearing gliomal tumors. T1 and T2 weighted MRI images were acquired to monitor tumor growth, at different time points. We developed an original multi-phase and multi-channel segmentation method, based on the level set framework of Chan and Vese, to facilitate the estimation of tumoral volumes. A clinical study comparing manual and segmented volumes on 18 mice demonstrate the adequacy of the multi-channel segmentation and its superiority over single-T1 channel automated segmentation in terms of measurement accuracy and correlation.

Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules

“…Coarse Segmentation For slices classified as "nodule slice", nodule candidates are screened within a spatial scope C defined by the most prominent blob in the NAM processed via watershed. They are then coarsely segmented using an iterated conditional mode (ICM) based multi-phase segmentation method [9], with the phase number equal to four as determined by global intensity distribution.…”

In vivo preclinical low field MRI monitoring of tumor growth following a suicide gene therapy in an orthotopic mice model of human glioblastoma

“…Moreover, and even if inter-and intra-observer differences in tumor volumes measured in this study were not statistically significant, reproducibility of manual methods for MR volume determination requires experienced observers. To overcome these difficulties, a vectorial multichannel-multiphase brain tumor segmentation technique aiming at reducing the number of operator dependent actions to the single choice of a volume of interest (VOI), which may include the whole brain, has been proposed recently by our group [27]. Application of such semi-automatic methods should greatly facilitate image data analysis in future works.…”