In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS),
which was organized in conjunction with the IEEE International Symposium on …

This article presents advanced algorithms for segmenting lung nodules, liver metastases,
and enlarged lymph nodes in CT scans. Segmentation and volumetry are essential tasks of a …

Automatic liver tumor segmentation would have a big impact on liver therapy planning
procedures and follow-up assessment, thanks to standardization and incorporation of full …

… Jan Hendrik Moltz: Provided expert annotations of the images to be used during training,
Validation, Testing phases of the challenge. Bruno Oliveira: Participated in the challenge, …

This article presents an algorithm for the segmentation of liver metastases in CT scans. It is
a hybrid method that combines adaptive thresholding based on a gray value analysis of the …

We present a fully automatic method employing convolutional neural networks based on the
2D U-net architecture and random forest classifier to solve the automatic liver lesion …

Simple Summary In malignant lymphoma an early and accurate diagnosis is essential for
therapy initiation and patient outcome. Within the diagnostic process, imaging plays a crucial …

In oncological chemotherapy monitoring, the change of a tumor's size is an important
criterion for assessing cancer therapeutics. Measuring the volume of a tumor requires its …

Simple Summary The use of immunotherapeutic agents significantly improved stage-IV
melanoma patients’ overall progression-free survival. To identify patients who do not benefit from …

Objectives To develop and validate a deep learning–based algorithm for segmenting and
quantifying the physiological and diseased aorta in computed tomography angiographies. …