Dr Ambra Morisi


PhD Candidate
DVM MRCVS GPCert (SASTS)

About

I graduated Cum Laude in Veterinary Medicine from the University of Bologna (Italy) in 2014. I completed one year of rotating internship at Istituto Veterinario di Novara (Italy). In February 2016, I joined the internship program at Fitzpatrick Referrals (UK). During this internship I also visited Davis University in California and I worked on clinical research projects. I worked in first opinion practice in the UK for one year while studying to obtain the certificate in Advanced Veterinary Practice. I have a particular interest in Surgical Oncology and Oncology. In January 2018, I began my current role as a PhD student at the University of Surrey School of Veterinary Medicine in collaboration with Fitzpatrick Referrals Oncology and Soft Tissue, CVSSP and Zoetis. As part of my PhD, I spent a period of time at Colorado State University and the University of Florida. I also teach undergraduate veterinary students in a clinical settings.

Research

Research interests

Teaching

Publications

TARANPREET RAI, AMBRA MORISI, BARBARA BACCI, NICHOLAS JAMES BACON, Michael J. Dark, Tawfik Aboellail, SPENCER A THOMAS, MIROSLAW Z BOBER, ROBERTO MARCELLO LA RAGIONE, KEVIN WELLS (2022)Deep learning for necrosis detection using canine perivascular wall tumour whole slide images, In: Scientific reports1210634

Abstract Necrosis seen in histopathology Whole Slide Images is a major criterion that contributes towards scoring tumour grade which then determines treatment options. However conventional manual assessment suffers from inter-operator reproducibility impacting grading precision. To address this, automatic necrosis detection using AI may be used to assess necrosis for final scoring that contributes towards the final clinical grade. Using deep learning AI, we describe a novel approach for automating necrosis detection in Whole Slide Images, tested on a canine Soft Tissue Sarcoma (cSTS) data set consisting of canine Perivascular Wall Tumours (cPWTs). A patch-based deep learning approach was developed where different variations of training a DenseNet-161 Convolutional Neural Network architecture were investigated as well as a stacking ensemble. An optimised DenseNet-161 with post-processing produced a hold-out test F1-score of 0.708 demonstrating state-of-the-art performance. This represents a novel first-time automated necrosis detection method in the cSTS domain as well specifically in detecting necrosis in cPWTs demonstrating a significant step forward in reproducible and reliable necrosis assessment for improving the precision of tumour grading.