  As an essential prerequisite for developing a medical intelligent assistant
system, medical image segmentation has received extensive research and
concentration from the neural network community. A series of UNet-like networks
with encoder-decoder architecture has achieved extraordinary success, in which
UNet2+ and UNet3+ redesign skip connections, respectively proposing dense skip
connection and full-scale skip connection and dramatically improving compared
with UNet in medical image segmentation. However, UNet2+ lacks sufficient
information explored from the full scale, which will affect the learning of
organs' location and boundary. Although UNet3+ can obtain the full-scale
aggregation feature map, owing to the small number of neurons in the structure,
it does not satisfy the segmentation of tiny objects when the number of samples
is small. This paper proposes a novel network structure combining dense skip
connections and full-scale skip connections, named UNet-sharp (UNet\#) for its
shape similar to symbol \#. The proposed UNet\# can aggregate feature maps of
different scales in the decoder sub-network and capture fine-grained details
and coarse-grained semantics from the full scale, which benefits learning the
exact location and accurately segmenting the boundary of organs or lesions. We
perform deep supervision for model pruning to speed up testing and make it
possible for the model to run on mobile devices; furthermore, designing two
classification-guided modules to reduce false positives achieves more accurate
segmentation results. Various experiments of semantic segmentation and instance
segmentation on different modalities (EM, CT, MRI) and dimensions (2D, 3D)
datasets, including the nuclei, brain tumor, liver, and lung, demonstrate that
the proposed method outperforms state-of-the-art models.
