Mert Yergin
mertmyergin
Statistics
- Member for 4 years, 3 months
- 64 challenge submissions
- 44 algorithms run
Activity Overview
Thyroid Nodule Segmentation and Classification
Challenge UserThe main topic of this TN-SCUI2020 challenge is finding automatic algorithms to accurately classify the thyroid nodules in ultrasound images. It will provide the biggest public dataset of thyroid nodule with over 4500 patient cases from different ages, genders, and were collected using different ultrasound machines. Each ultrasound image is provided with its ground truth class (benign or maglinant) and a detailed delineation of the nodule. This challenge will provide a unique opportunity for participants from different backgrounds (e.g. academia, industry, and government, etc.) to compare their algorithms in an impartial way.
A-AFMA
Challenge UserPrenatal ultrasound (US) measurement of amniotic fluid is an important part of fetal surveillance as it provides a non-invasive way of assessing whether there is oligohydramnios (insufficient amniotic fluid) and polyhydramnios (excess amniotic fluid), which are associated with numerous problems both during pregnancy and after birth. In this Image Analysis Challenge, we aim to attract attention from the image analysis community to work on the problem of automated measurement of the MVP using the predefined ultrasound video clip based on a linear-sweep protocol [1]. We define two tasks. The first task is to automatically detect amniotic fluid and the maternal bladder. The second task is to identify the appropriate points for MVP measurement given the selected frame of the video clip, and calculate the length of the connected line between these points. The data was collected from women in the second trimester of pregnancy, as part of the PURE study at the John Radcliffe Hospital in Oxford, UK.
