The human mind is exceptional when it comes to recognizing things in an instant: identify body parts, seeing something that doesn't belong, a pattern which is consistent with cancer, etc. In AI-speak the above could be considered segmentation, anomaly detection, classification, etc. But AI segmentation is more than just identifying an area; it can fully mark that area, creating a digital mask that covers the area of interest. And this is precisely something that -while not impossible for a human- would take a lot of time (and with a lot of variability). The true value of Deep Learning Segmentation therefore is its unique capability to instantly create segmentation masks, which allow accurate quantification.

A segmentação de imagem desempenha um papel fundamental em uma variedade de aplicações em diferentes campos, desde a medicina até a visão computacional e a robótica. Ao dividir uma imagem em regiões distintas, os pesquisadores e profissionais podem extrair informações relevantes e insights úteis para uma ampla gama de finalidades. Por exemplo, na medicina, a segmentação de imagens é frequentemente usada em exames de imagem para auxiliar no diagnóstico e tratamento de doenças, permitindo a identificação precisa de estruturas anatômicas e anomalias.

In my experience (incl. modern Deep Learning segmentation but also pre-DL segmentation many years ago with ultrasound cardiac border segmentation for LV function assessment) clearly the #1 driver for AI segmentation in medical imaging is increasing HCP's productivity. When properly implemented, AI segmentation enables mayor time savings. But the value of increased consistency and accuracy is also very high, especially now that some segmentation AI models start to beat the "average doctor" in terms of accuracy, getting close to performing as well as the "best doctors". With the extra benefit of doing so with incredible consistency. Another mayor opportunity is for interactive medical imaging Educational tools.

In a medical context, just identifying an area ("here is a tumor") is not sufficient; in a modern care flow it is necessary to get accurate (sometimes volumetric) calculations: how big is this tumor? do I have the exact area marked out so I can do radiotherapy (RT)? in my experience, spotting the tumor is easy and fast for a trained human, but AI is much faster, accurate and repeatable when it comes to creating a detailed mask of the tumor. Or calculating growth stats.

Além disso, a utilização da IA para segmentação de imagens permite uma abordagem mais escalável e flexível, capaz de lidar com grandes volumes de dados de forma eficiente e rápida. Isso é especialmente relevante em áreas como medicina, onde a análise de grandes conjuntos de imagens médicas pode ser demorada e exigir recursos consideráveis. Com algoritmos de IA, é possível processar essas imagens em tempo hábil, permitindo diagnósticos mais rápidos e precisos, o que pode resultar em melhores resultados para os pacientes.

Os algoritmos de IA utilizados para segmentação de imagens operam por meio de um processo iterativo e adaptativo, onde o modelo é exposto a uma ampla variedade de exemplos durante a fase de treinamento. Durante esta etapa, o algoritmo de aprendizado de máquina ou aprendizagem profunda ajusta seus parâmetros internos para otimizar sua capacidade de identificar e delinear corretamente os diferentes segmentos na imagem. Isso é feito através de um processo de retropropagação, onde o modelo compara suas previsões com os segmentos reais fornecidos nos dados de treinamento e ajusta seus pesos e conexões de acordo com os erros cometidos.

There are a variety of manuals or platforms that allow you to create a segmentation algorithm "in 3 easy steps". While perfectly OK for a proof of concept, the steps from there to a clinically relevant AI pipeline are all about inspiration and transpiration: it's data scientists working every step of the way to squeeze the best out of the available data, keeping the clinical context in mind. Depending on the use case, a choice of sensitivity and specificity will be required. How do the specialists want to see the output? How should it fit into their workflow? Bridging the gap between the world of data science and clinical specialists is and remains a human business.

From a healthcare professional's point of view, the challenges include: ✦Clear explanations: To trust AI's segmentation, clinicians need to understand its reasoning, not just the answer. ✦Unbiased data: Training data needs to be diverse to avoid biased segmentation, especially for underrepresented groups. ✦Real-world generalizability: AI should adapt to the complexities of daily practice, handling diverse patients and image variations. ✦Smooth workflows: AI tools should integrate seamlessly, not disrupt existing workflows. Time saved using AI should outweigh the time spent interacting with it. ✦Strong validation: Robust clinical trials are vital to prove AI's effectiveness and safety in real-world settings.

The workhorse for AI-powered segmentation is a type of deep neural network called a convolutional neural network (CNN). CNNs are trained on massive datasets of medical images that have already been manually segmented by experts. During training, the CNN learns to identify patterns and relationships between image features (pixels, intensity levels, textures) and the corresponding segmented structures. CNNs draw inspiration from the structure and function of the visual cortex in the animal brain. The visual cortex processes visual information in a hierarchical manner, with simpler features being detected in earlier layers and more complex features emerging in later layers.