Publications by authors named "Maximilian Rattunde"
In the present work, we present a publicly available, expert-segmented representative dataset of 158 3.0 Tesla biparametric MRIs [1]. There is an increasing number of studies investigating prostate and prostate carcinoma segmentation using deep learning (DL) with 3D architectures [2], [3], [4], [5], [6], [7].
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- The study explored differences between men and women with renal cell carcinoma (RCC), focusing on factors like abdominal fat, muscle density, tumor characteristics, and survival rates.
- It included 470 patients who underwent nephrectomy from 2006 to 2019 and found that women had more subcutaneous fat, while men had more visceral fat and higher muscle density.
- Results indicated that higher psoas muscle index linked to lower tumor grades and better survival, especially in men, while abdominal fat did not significantly impact tumor characteristics or outcomes.
Background: The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability.
Methods: Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps.