  Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that
provides 3-4 times higher resolution than traditional ultrasound, potentially
enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate
segmentation is crucial for prostate volume measurement, cancer diagnosis,
prostate biopsy, and treatment planning. However, prostate segmentation on
micro-US is challenging due to artifacts and indistinct borders between the
prostate, bladder, and urethra in the midline. This paper presents MicroSegNet,
a multi-scale annotation-guided transformer UNet model designed specifically to
tackle these challenges. During the training process, MicroSegNet focuses more
on regions that are hard to segment (hard regions), characterized by
discrepancies between expert and non-expert annotations. We achieve this by
proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns
a larger weight to prediction errors in hard regions and a lower weight to
prediction errors in easy regions. The AG-BCE loss was seamlessly integrated
into the training process through the utilization of multi-scale deep
supervision, enabling MicroSegNet to capture global contextual dependencies and
local information at various scales. We trained our model using micro-US images
from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model
achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm,
outperforming several state-of-the-art segmentation methods, as well as three
human annotators with different experience levels. Our code is publicly
available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly
available at https://zenodo.org/records/10475293.
