Home Community University Hospital of Basel Unveils TotalSegmentator: A Deep Learning Segmentation Model that may Mechanically Segment Major Anatomical Structures in Body CT Images

University Hospital of Basel Unveils TotalSegmentator: A Deep Learning Segmentation Model that may Mechanically Segment Major Anatomical Structures in Body CT Images

University Hospital of Basel Unveils TotalSegmentator: A Deep Learning Segmentation Model that may Mechanically Segment Major Anatomical Structures in Body CT Images

The variety of CT scans performed and the info processing capability available have grown over the past several years. Due to developments in deep learning approaches, the potential of image evaluation algorithms has been greatly enhanced. Consequently of improvements in data storage, processing speed, and algorithm quality, larger samples have been utilized in radiological research. Segmentation of anatomical structures is crucial to a lot of these investigations. Radiological image segmentation might be used for advanced biomarker extraction, automatic pathology detection, and tumor load quantification. Segmentation is already utilized in common clinical evaluation for purposes like surgery and radiation planning. 

Separate models exist for segmenting individual organs (equivalent to the pancreas, spleen, colon, or lung) on CT images, and research has also been done on combining data from multiple anatomical structures right into a single model. Nevertheless, all previous models include only a small subset of essential anatomical structures and are trained on tiny datasets not representative of routine clinical imaging. The shortage of accessibility to many segmentation models and datasets severely limits their usefulness to researchers. Access to publicly available datasets often necessitates lengthy paperwork or requires the use of information providers which might be either cumbersome to work with or rate-limited.

Researchers on the Clinic of Radiology and Nuclear Medicine, University Hospital Basel, used around 1204 CT datasets to create a way for segmenting 104 anatomical entities. They acquired the dataset with CT scanners, acquisition settings, and contrast phases. Their model, TotalSegmentator, can segment a lot of the body’s anatomically necessary structures with minimal user input, and it does so reliably in any clinical environment. High accuracy (Dice rating of 0.943) and robustness on various clinical data sets make this tool superior to others freely available online. The team also used an enormous dataset of over 4000 CT examinations to look at and report age-related changes in volume and attenuation in various organs. 

The researchers have made their model available as a pre-trained Python package so anyone can use it. They highlight that since their model uses lower than 12 GB of RAM and a GPU is unnecessary, it may be run on any standard computer. Their dataset can also be easily accessible, requiring no special permissions or requests to download it. The present research used a nnU-Net-based model since it has been proven to provide reliable results across various tasks. It’s now considered the gold standard for medical picture segmentation, surpassing most other approaches. Hyperparameter adjustment and the investigation of various models, equivalent to transformers, enhance the performance of the usual nnU-Net.

As mentioned of their paper, the proposed model has various possible uses. Along with its obvious surgical applications, quick and simply accessible organ segmentation enables individual dosimetry, as demonstrated for the liver and kidneys. Moreover, automated segmentation can improve research by providing clinicians with normal and even age-dependent parameters (HU, volume, etc.). Together with a lesion-detection model, their model could be utilized to approximate tumor load for a given body part. Moreover, the model can function a foundation for developing models designed to discover various diseases. 

The model has been downloaded by over 4,500 researchers to be used in various contexts. Only recently was analyzing data sets of this size possible, and it took loads of effort and time from data scientists. This work has demonstrated associations between age 12 and the quantity of diverse segmented organs using a dataset of over 4000 individuals who had undergone a CT polytrauma scan. Common literature figures for normal organ sizes and age-dependent organ growth are typically based on sample sizes of just a few hundred people. 

The team highlights that male patients were overrepresented within the study datasets, which could also be because more men than women visit hospitals on average. Nevertheless, the team believes their model generally is a start line for more extensive investigations of radiology populations. They mention that future studies will include more anatomical structures of their dataset and model. As well as, they’re recruiting additional patients, adjusting for potential confounders, and conducting further correlation analyses to conduct a more comprehensive study of aging.

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Dhanshree Shenwai is a Computer Science Engineer and has an excellent experience in FinTech firms covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is passionate about exploring latest technologies and advancements in today’s evolving world making everyone’s life easy.

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