Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.

Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal post-operative brain scans (T1 MRI, post-Gadolinium T1 MRI, and T2-FLAIR images) of 15 patients who were treated with post-operative radiation therapy, along with manual annotations of their tumour subregions. A 3D densely-connected U-net was developed for segmentation of brain tumour regions and extensive experiments were conducted to enhance model accuracy. A model was initially developed using the publicly available BraTS dataset consisting of pre-operative brain scans. This model achieved Dice Scores of 0.90, 0.83 and 0.78 for predicting whole tumour, tumour core, and enhancing tumour subregions when tested on BraTS20 blind validation dataset. The acquired knowledge from BraTS was then transferred to the local dataset. For augmentation purpose, the local dataset was registered to a dataset of MRI brain scans of healthy subjects. To improve the robustness of the model and enhance its accuracy, ensemble learning was used to combine the outputs of all the trained models. Even though the size of the dataset is very small, the final model can segment brain tumours with a high Dice Score of 0.83, 0.77 and 0.60 for whole tumour, tumour core and enhancing core respectively.

Automated Brain Tumor Segmentation Using Multimodal Brain Scans: A Survey Based on Models Submitted to the BraTS 2012-2018 Challenges.

Reliable brain tumor segmentation is essential for accurate diagnosis and treatment planning. Since manual segmentation of brain tumors is a highly time-consuming, expensive and subjective task, practical automated methods for this purpose are greatly appreciated. But since brain tumors are highly heterogeneous in terms of location, shape, and size, developing automatic segmentation methods has remained a challenging task over decades. This paper aims to review the evolution of automated models for brain tumor segmentation using multimodal MR images. In order to be able to make a just comparison between different methods, the proposed models are studied for the most famous benchmark for brain tumor segmentation, namely the BraTS challenge [1]. The BraTS 2012-2018 challenges and the state-of-the-art automated models employed each year are analysed. The changing trend of these automated methods since 2012 are studied and the main parameters that affect the performance of different models are analysed.

Ranitidine induces inhibition and structural changes in sucrase.

Ranitidine is an antagonist of histamine-2 (H(2)) receptor. It is employed to treat peptic ulcer and other conditions in which gastric acidity must be reduced. Sucrase is a hydrolytic enzyme that catalyzes the breakdown of sucrose to its monomer content. A liquid of yeast sucrase was developed for treatment of congenital sucrase-isomaltase deficiency (CSID) in human. In this study, the effect of ranitidine on yeast sucrase activity was investigated. Our results showed that ranitidine binds to sucrase and inhibits the enzyme in a noncompetitive manner. The K(i) and IC(50) values were measured to be about 2.3 and 2.2 mM, respectively. Fluorescence measurement showed conformational changes after binding of ranitidine to the enzyme. The fluorescence spectra showed that ranitidine could bind to both free enzyme and enzyme-substrate complex, which was accompanied with reduction of emission intensity and red shift production.

Structural changes and inhibition of sucrase after binding of scopolamine.

Scopolamine (hyoscine) is commonly used as an anticholinergic drug to relieve nausea, vomiting and dizziness of a motion sickness as well as recovery from anesthesia and surgery. Sucrase as a hydrolytic enzyme breaks down sucrose into its monomers, glucose and fructose. The aim of this study was to evaluate the effect of scopolamine on the activity and the structural changes of yeast sucrase. The results showed that binding of scopolamine to sucrase could inhibit the enzyme activity. A non-competitive inhibition was observed in different concentrations of scopolamine (0.6 to 3.6mM). The study by circular dichroism measurement in far-UV showed that the absolute enzyme exhibited a flat negative trough, indicating the presence of alpha-helices and beta-sheet structures in the enzyme. Binding of the inhibitor on the enzyme made a deeper trough at 218nm, suggesting the increasing of beta-sheet content of the enzyme. Fluorescence measurement showed that binding of scopolamine to free enzyme and enzyme-substrate complex increased the peak intensity at 350nm and also induced red shift. Our findings suggest that scopolamine binds to the location other than the active site of enzyme and that the binding causes structural changes and inhibits the enzyme activity.