Dr Ambra Morisi

PhD Candidate


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 interests



Ambra Morisi, Taran Rai, Nicholas J. Bacon, Spencer A. Thomas, Miroslaw Bober, Kevin Wells, Michael J. Dark, Tawfik Aboellail, Barbara Bacci, Roberto M. La Ragione (2023)Detection of Necrosis in Digitised Whole Slide Images for Better Grading of Canine Soft Tissue Sarcoma Using Machine-Learning, In: Veterinary sciences10(1)45

The definitive diagnosis of canine soft-tissue sarcomas (STSs) is based on histological assessment of formalin-fixed tissues. Assessment of parameters, such as degree of differentiation, necrosis score and mitotic score, give rise to a final tumour grade, which is important in determining prognosis and subsequent treatment modalities. However, grading discrepancies are reported to occur in human and canine STSs, which can result in complications regarding treatment plans. The introduction of digital pathology has the potential to help improve STS grading via automated determination of the presence and extent of necrosis. The detected necrotic regions can be factored in the grading scheme or excluded before analysing the remaining tissue. Here we describe a method to detect tumour necrosis in histopathological whole-slide images (WSIs) of STSs using machine learning. Annotated areas of necrosis were extracted from WSIs and the patches containing necrotic tissue fed into a pre-trained DenseNet161 convolutional neural network (CNN) for training, testing and validation. The proposed CNN architecture reported favourable results, with an overall validation accuracy of 92.7% for necrosis detection which represents the number of correctly classified data instances over the total number of data instances. The proposed method, when vigorously validated represents a promising tool to assist pathologists in evaluating necrosis in canine STS tumours, by increasing efficiency, accuracy and reducing inter-rater variation.

Taranpreet Rai, Ambra Morisi, Barbara Bacci, Nicholas J. Bacon, Michael J. Dark, Tawfik Aboellail, Spencer A. Thomas, Miroslaw Bober, Roberto La Ragione, Kevin Wells (2022)Deep learning for necrosis detection using canine perivascular wall tumour whole slide images, In: Scientific Reports1210634

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.