U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Brain segmentation is often the first and most critical step in quantitative analysis of the brain
for many clinical applications, including fetal imaging. Different aspects challenge the …

Glaucoma is an eye condition that leads to loss of vision and blindness if not diagnosed in
time. Diagnosis requires human experts to estimate in a limited time subtle changes in the …

Fetal brain segmentation and gestational age prediction have been under active research in
the field of medical image processing for a long time. However, both these tasks are …

Fetal brain segmentation has been a field of interest since a long time. However, it is a
challenging task as well for reasons, like blurred images due to fetal motion. Recently, deep …

We investigate various channel encoding techniques applied to breast density classification
in mammograms; specifically, local binary, ternary, and quinary encoding approaches are …

The extraction of the fetal brain from magnetic resonance (MR) images is a challenging task.
In particular, fetal MR images suffer from different kinds of artifacts introduced during the …

Fetal brain segmentation is an important first step for slice-level motion correction and slice-
to-volume reconstruction in fetal MRI. Fast and accurate segmentation of the fetal brain on …

Fetal brain extraction from magnetic resonance (MR) images is of great importance for both
clinical applications and neuroscience studies. However, it is a challenging task, especially …

Automatic lesion segmentation in ultrasound helps diagnose diseases. Segmenting lesion
regions accurately from ultrasound images is a challenging task due to the difference in the …