Published: 9 October 2023| Version 1 | DOI: 10.17632/y7ydsj63p4.1
Dominik Vilimek, Jakub Stefansky, Radana Vilimkova Kahankova, Martin Blaho, Milos Golian, Jan Kubicek, Marek Buzga, Robert Psar, Ondrej Urban, Radek Martinek


Pancreatic assessment through magnetic resonance imaging (MRI) has become increasingly vital for diagnosing anomalies and pathologies, serving as a crucial prerequisite for numerous clinical applications, including diabetes inspection and surgical planning. However, automating pancreas segmentation in medical images remains a formidable challenge. In this paper, we introduce an innovative automated pancreas segmentation approach for MRI images utilizing 2D deep learning. Our methodology employs U-Net, ResU-Net, and nnU-Net architectures, capable of segmenting diverse pancreas samples and identifying pancreas presence/absence in adjacent slices, eliminating the need for manual region of interest or slice selection. Furthermore, we provide a unique and freely available dataset comprising MRI images from 37 patients post-bariatric surgery, greatly enhancing reproducibility and accessibility for AI model development. Our results demonstrate ResU-Net's superior performance with a Dice coefficient of 77.6% (36.55%), and Jaccard index of 69.36% (40.19%). Additionally, we offer a comprehensive critical analysis of the quantification of fatty infiltration, shedding light on potential biases when choosing individual models. Our contribution includes not only a high-quality unique dataset but also an automated approach that simplifies pancreas segmentation, advancing the field's research and clinical applications. Overview Pancreatic MRI images hold a large amount of useful information for medical diagnostics and research. In spite of this, segmenting these images, particularly in order to quantify fat fractions, can be challenging. A complex task is made more challenging by the unique nuances of the pancreas, along with the presence of surrounding fat and exclusions of the pancreatic duct. To advance research in this area, we present the following dataset: 37 Patients: A diverse range of scans that bring forth the variance needed for robust model training. Image Quality Assurance: Every scan of the T1 VIBE DIXON sequence underwent rigorous image quality assessment. Manual Annotations: To save researchers manual labor, the dataset comes with pancreas segmentations, double-checked by seasoned radiologists for accuracy. Motivation This dataset aims to be the bases for researchers looking to pioneer advancements in the area of precise pancreatic fat fraction quantification. Our goal is to provide users with comprehensive, quality-assured collections of images and annotations to enable them to develop innovative solutions, evaluate models, and ultimately improve medical outcomes. Dataset Structure Annotations: Contains annotations for each patient. Data Each patient has a dedicated folder, further divided by the type of MRI sequence. Raw_Dixon_Data: Water and Fat Dixon images, each containing data identified by patient IDs. Preprocessed_dataset: Contains preprocessed data organized by patient IDs.



Vysoka Skola Banska-Technicka Univerzita Ostrava Fakulta Elektrotechniky a Informatiky


Magnetic Resonance Imaging, Pancreas, Pancreatitis, Medical Image Processing, Deep Learning