3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction

Past few years have witnessed the prevalence of deep learning in many application scenarios, among which is medical image processing. Diagnosis and treatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper, a 3D U-net based deep learning model has been trained with the help of brain-wise normalization and patching strategies for the brain tumor segmentation task in the BraTS 2019 competition. Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0.737, 0.807 and 0.894 respectively on the validation dataset. These three values on the test dataset are 0.778, 0.798 and 0.852. Furthermore, numerical features including ratio of tumor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.Comment: Third place award of the 2019 MICCAI BraTS challenge survival task [BraTS 2019](https://www.med.upenn.edu/cbica/brats2019.html

Proposing a Tool for Supply Chain Configuration: An Application to Customised Production

The full implementation of collaborative production networks is crucial for companies willing to respond to consumer demand strongly focused on product customisation. This chapter proposes an approach to evaluate the performance of different Supply Chain (SC) configurations in a customised production context. The model is based on discrete-event simulation and is applied to the case of supply chain in the fashion sector to support the comparison between mass and customised production. A prototype web-based interface is also developed and proposed to facilitate the use of the model not only for experts in simulation but for any user in the SC management field

Choroid plexus tumours

Choroid plexus tumours are rare epithelial brain tumours and limited information is available regarding their biology and the best treatment. A meta-analysis was done to determine prognostic factors and the influence of various treatment modalities. A thorough review of the medical literature (1966–1998) revealed 566 well-documented choroid plexus tumours. These were entered into a database, which was analysed to determine prognostic factors and treatment modalities. Most patients with a supratentorial tumour were children, while the most common sites in adults were the fourth ventricle and the cerebellar pontine angle. Cerebellar pontine angle tumours were more frequently benign. Histology was the most important prognostic factor, as one, five, and 10-year projected survival rates were 90, 81, and 77% in choroid plexus-papilloma (n=353) compared to only 71, 41, and 35% in choroid plexus-carcinoma respectively (P<0.0005). Surgery was prognostically relevant for both choroid plexus-papilloma (P=0.0005) and choroid plexus-carcinoma (P=0.0001). Radiotherapy was associated with significantly better survival in choroid plexus-carcinomas. Eight of 22 documented choroid plexus-carcinomas responded to chemotherapy. Relapse after primary treatment was a poor prognostic factor in choroid plexus-carcinoma patients but not in choroid plexus-papilloma patients. Treatment of choroid plexus tumours should start with radical surgical resection. This should be followed by adjuvant treatment in case of choroid plexus-carcinoma, and a ‘wait and see’ approach in choroid plexus-papilloma