Among deep learning-driven approaches, U-Net derived models dominate in organ segmentation tasks in the abdomen17,18 where public datasets are abundant (liver, spleen and kidney). For serial OARs (duodenum, stomach, and small bowel) in pancreatic cancer treatment, a few U-Net based models ...
Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic...
Each color in the bottom image represent anatomically distinct brain regions in the atlas; major areas showing decreased ADC (top image; red arrows) aligned with the left (brown) and right (light green) PVN in the bottom image (red arrows). c Data showing the percent change of the number...
For both hemisegments, the dorsal midline is at the top, the ventral midline at the bottom, anterior is to the left. The location of sensilla is indicated by graphic symbols (see key at bottom right of figure). Somatic muscle fibers are shown in gray. For details on the pattern see ...
In insects, chordotonal organs are found in all major body regions (head, thorax, abdomen) and many appendages (antennae, mouthparts, legs, wings, ovipositors, cerci). Modalities include joint proprioception (connective chordotonal organs), substrate vibration (subgenual organs), sound (tympanal or...
Consid- erable distribution mismatch between labeled and unlabeled im- ages can be observed (top), while our method can well improve the distribution mismatch (bottom). Best viewed zoom in. partitioned small-cubes are randomly mixed while ignoring t...
with the abdomen terminating at ventrite 7, the second of the two light organ-bearing segments in males7. Adult light organs consist of three distinctive layers: cuticle, photogenic layer, and dorsal layer or uric acid layer3. Multiple photocytes or luminescent cells surround the tracheal end ...
The pseudo-labels output from the middle segmentation network is used to supervise the learning progress of the desired model (bottom segmentation network). In the second stage, the circular learning and pixel-adaptive mask refinement are used to further improve the desired model performance. With ...