New dataset version uploaded in Zenodo: https://zenodo.org/records/12687192

Dataset

The challenge cohort consists of 90 CT images prospectively gathered at the University Hospital Erlangen between August 2023 and October 2023. Each CT will have multiple classes: background (0), pancreas (1), kidney (2) and liver (3). In addition, each of the CTs will have three different annotators from different experts that will contain the four classes specified previously.

• Training Phase cohort:
  

20 CT scans belonging to group A with the respective annotations will be given. It is encouraged to leverage publicly available external data annotated by multiple raters. QUIBQ21 organizers have already been contacted and have given consent (with proper attribution) on using their multi-annotator data. The idea of giving a small amount of data for the training set and giving the opportunity of using a public dataset for training is to make the challenge more inclusive, giving the option to develop a method by using data that is in anyone's hands. Furthermore, by using this data to train and using other data to evaluate, it makes it more robust to shifts and other sources of variability between datasets.

The training set has been published in Zenodo: https://zenodo.org/records/12687192

• Validation Phase cohort:
  

5 CT scans belonging to group A will be used for this phase.

• Test Phase cohort:
  

65 CT scans will be used for evaluation. 20 CTs belonging to group A, 22 CTs belonging to group B and 23 CTs belonging to group C.

Both validation and testing CT scans cohorts will not be published until the end of the challenge. Furthermore, to which group each CT scan belongs will not be revealed until after the challenge.

Clinical specifications

Inclusion criteria were a maximum of 10 cysts with a diameter of less than 2,0 cm. Furthermore, CT scans with major artifacts (e.g. breathing artifacts) or incomplete registrations were excluded.

Participants were required to be over 18 years old and provide both verbal and written consent for the use of their CT images in the Challenge. Both study-specific and broad consent were obtained. Among the 90 patients, there were 51 males and 39 females, aged between 37 and 94 years, with an average age of 65.7 years. All patients received treatment at the University Hospital Erlangen in Bavaria, Germany. No additional selection criteria were set to ensure a representative sample of a typical patient cohort.

Our overall data consists on 90 CTs splitted in three different groups:

• Group A: cases with 2 cysts or less with no contour altering pathologies - 45 CTs

• Group B: cases with 3-5 cysts with no contour altering pathologies - 22 CTs

• Group C: cases with 6-10 cysts with some pathologies included (liver metastases, hydronephrosis, adrenal gland metastases, missing kidney) - 23 CTs

However, in any case, the participants will not know which case belongs to which group. This information will be released after the challenge, together with the whole dataset.

Technical specifications

The CTs used needed to be contrast-enhanced CT scans in a portal venous phase with the acquisition of thin slices ranging from 0.6 to 1mm. Thoracic-Abdominal CT images were taken during the patients' hospital stay, motivated by various medical needs. Given the focus on abdominal organs, the Br40 soft kernel was employed. CT examinations were conducted using SIEMENS CT scanners at the university hospital Erlangen, with rotation speeds of 0.25 or 0.5 sec. Detector collimation varied from 128x0.6mm single source to 98x0.6x2 and 144x0.4x2 dual source configurations. Spiral pitch factors ranged from 0.3 to 1.3. The mean reference tube current was set at 200 mAs, adjustable to 120 mAs. Automated tube voltage adaptation and tube current modulation were implemented in all instances. Contrast agent administration was standard practice, with an injection rate of 3-4 mL/s and a body weight-adjusted dosage of 400 mg(iodine)/kg (equivalent to 1.14 ml/kg Iomeprol 350mg/ml). All images underwent reconstruction using soft convolution kernels and iterative techniques.

The data usage agreement for this challenge is CC BY-NC (Attribution-NonCommercial).

The data collected for the generation of the datasets involved in this challenge has been approved by an ethical committee (number 23-243-B) held at the Universitätsklinikum Erlangen Hospital.

The data to be used during and after the challenge is pseudonymized and coded by the Hospital to assure that a re-identification of the data sample is not possible. Moreover, the patient information is only known by the IP of the Hospital so that the challenge collaborators do not have as well any means to identify patient's data at any point.