Comparison of Conventional Cyclophosphamide versus Fludarabine-Based Conditioning in High-Risk Aplastic Anemia Patients Undergoing Matched-Related Donor Transplantation

Allogeneic stem cell transplant for high-risk aplastic anemia (AA) yields inferior results using conventional cyclophosphamide (CY)-based conditioning. The use of fludarabine (Flu)-based regimens has resulted in improved outcomes in high-risk patients. Limited data are available comparing these two conditioning regimens in such patients. We retrospectively analyzed 192 high-risk patients undergoing matched-related donor transplantation from July 2001 to December 2018. The median age was 19.5 (2–52) years. Patients were divided into 2 groups, Cy200 anti-thymocyte globulin (ATG)20 (Gp1 n = 79) or Flu120–150 Cy120–160 ATG20 (Gp2 n = 113). The risk of graft failure was significantly higher in Gp1, and the majority occurred in patients with >2 risk factors (p = 0.02). The incidence of grade II-IV acute graft versus host disease (GVHD) and chronic GVHD was not significantly different between the two groups. The overall survival (OS) of the study cohort was 81.3 %, disease-free survival (DFS) 76.6 % and GVHD-free relapse-free survival (GRFS) was 64.1%. DFS and GRFS were significantly higher in Gp2 as compared to Gp1: DFS 84.1% versus 68.4 % (p = 0.02), GRFS 77.9% versus 54.4% (p = 0.01), respectively. We conclude that Flu-based conditioning is associated with superior OS, DFS and GRFS as compared to the conventional Cy-based regimen in high-risk AA

Skull stripping using traditional and soft-computing approaches for magnetic resonance images : a semi-systematic meta-analysis

MRI scanner captures the skull along with the brain and the skull needs to be removed for enhanced reliability and validity of medical diagnostic practices. Skull Stripping from Brain MR Images is significantly a core area in medical applications. It is a complicated task to segment an image for skull stripping manually. It is not only time consuming but expensive as well. An automated skull stripping method with good efficiency and effectiveness is required. Currently, a number of skull stripping methods are used in practice. In this review paper, many soft-computing segmentation techniques have been discussed. The purpose of this research study is to review the existing literature to compare the existing traditional and modern methods used for skull stripping from Brain MR images along with their merits and demerits. The semi-systematic review of existing literature has been carried out using the meta-synthesis approach. Broadly, analyses are bifurcated into traditional and modern, i.e. soft-computing methods proposed, experimented with, or applied in practice for effective skull stripping. Popular databases with desired data of Brain MR Images have also been identified, categorized and discussed. Moreover, CPU and GPU based computer systems and their specifications used by different researchers for skull stripping have also been discussed. In the end, the research gap has been identified along with the proposed lead for future research work

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI)