deep learning based segmentation of breast masses in dedicated breast ct imaging radiomic feature stability between radiologists and artificial intelligence

Abstract A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks' lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation

Definição de uma biblioteca para apoio à decisão de avaliação de orelhas proeminentes.

A utilização de ferramentas digitais, como apoio na prática da ciência médica, começa cada vez mais a ganhar importância, à medida que a tecnologia em geral vai ganhando maturidade, e que os profissionais de saúde também vão ganhando confiança nas mesmas. De tal forma que os profissionais de saúde já começam a procurar software desenvolvido à medida das suas necessidades, não ficando à espera que as empresas que dominam o mercado disponibilizem sistemas que lhes podem (ou não) ser úteis. O objetivo servir de apoio a médicos para o cálculo de um índice fotográfico digital, desenvolvido para auxiliar na decisão clínica formal que motiva a indicação para um procedimento cirúrgico eletivo estético em idade pediátrica, designado por Otoplastia. Para tal, foi necessário criar um sistema que detete da forma mais automática e precisa possível a posição das orelhas, auxiliando no cálculo de medidas fotográficas digitais, de forma a ser averiguada a protrusão auricular nas respetivas imagens, auxiliando na sua caracterização clínica, servindo de suporte num modelo de apoio à decisão clínica para proposição de intervenção cirúrgica corretiva. O valor do índice calculado foi obtido mediante o trabalho de Doutoramento do especialista em cirurgia pediátrica Mestre José Lopes do Santos, utilizando apenas software livre e de código aberto, assim como vocacionado para dispositivos móveis com o sistema operativo Android. Para concretização do objetivo proposto, foi explorado o OpenCV como sistema de processamento de imagem, dada a sua portabilidade para várias plataformas, tendo sido analisadas e aprimoradas diversas abordagens para a deteção automática de posicionamento de elementos faciais. A solução mobile desenvolvida foi avaliada comparando os resultados obtidos com os valores do método de medição digital tradicional calculados através do computados pessoal, tendo contribuindo com sucesso para um mais eficaz tempo de consulta.The usage of digital tools, as medical practice support, is constantly gaining importance as technology evolves, and as health care professionals start gaining confidence on such tools. This is patent in such ways that these professionals are starting to search for custom made software, which suits their needs, instead of waiting for the dominant players in medical software to release systems that may (or may not) be useful to their practice. This is the case of this Masters’ project, which will serve as support to healthcare professionals for the calculation of a photographic index, created to assert the real necessity for a corrective surgical intervention on infants. For such task, an automatic and as precise as possible ear position system will be developed, which shows the index value (automatically calculated) to evaluate if the patient’s ears can be classified as “prominent ears” or not, and by that decide if such patient is a otoplasty surgery candidate or not. The challenge for this work is to, as mentioned, detect and mark as accurately as possible the region of both the patient’s ears, allowing Doctors to easily define manually the exact area of each ear, giving after that the calculated index, based on the Doctoral work of MasterJosé Lopes dos Santos, using only open-source and free software, and directed to mobile devices (with initial focus on Android devices). To tackle on this challenge, OpenCV as image processing system will be explored, due to its portability, and also analyzed the best approaches for automatic head features estimation. To assert the developed solution, a comparison between the efficiency of the developed application and the hand-made calculation done by a doctor will be made