OBJECTIVES: This paper aims at introducing a novel approach for segmentation of overlapping objects and at demonstrating its applicability to medical images. METHODS: This work details a novel approach enhancing the known theory of full-segmentation of an image into regions by lifting it to a semantic segmentation into objects. Our theory allows the formal description of partitioning an image into regions on the first level and allowing the occurrence of overlaps and occlusions of objects on a second, semantic level. Possible applications for the use of this 'semantical segmentation' are the analysis of radiographs and micrographs. We demonstrate our approach by the example of segmentation and separation of overlapping cervical cells and cell clusters on a set of 787 image pairs of registered PAP- and DAPI-stained micrographs. The semantical cell segmentation yielding areas of cell plasmas and nuclei are compared to a manual segmentation of the same images, where 2212 cells have been labeled. A direct comparison of over and under-segmentation between the two segmentation sets yields a mean difference value of 10.15% for the nuclei and 10.80% for the plasma. RESULTS: Using the proposed theory of semantical segmentation of images in combination with adequate models of the image contents, our approach allows identifying, separating and distinguishing several overlapping, occluding objects in medical images. Applying the proposed theory to the application of cervical cell segmentation from overlapping cell clusters and aggregates, it can be seen that it is possible to formally describe the complex image contents. CONCLUSIONS: The proposed method of semantical segmentation is a mighty tool and under the assumption of the subtractive transparency model can be used in different medical image processing applications such as radiology and microscopy. By using alternative models to solve the ambiguities attached to overlaps and occlusions, further fields of application can be addressed.
OBJECTIVES: This paper aims at introducing a novel approach for segmentation of overlapping objects and at demonstrating its applicability to medical images. METHODS: This work details a novel approach enhancing the known theory of full-segmentation of an image into regions by lifting it to a semantic segmentation into objects. Our theory allows the formal description of partitioning an image into regions on the first level and allowing the occurrence of overlaps and occlusions of objects on a second, semantic level. Possible applications for the use of this 'semantical segmentation' are the analysis of radiographs and micrographs. We demonstrate our approach by the example of segmentation and separation of overlapping cervical cells and cell clusters on a set of 787 image pairs of registered PAP- and DAPI-stained micrographs. The semantical cell segmentation yielding areas of cell plasmas and nuclei are compared to a manual segmentation of the same images, where 2212 cells have been labeled. A direct comparison of over and under-segmentation between the two segmentation sets yields a mean difference value of 10.15% for the nuclei and 10.80% for the plasma. RESULTS: Using the proposed theory of semantical segmentation of images in combination with adequate models of the image contents, our approach allows identifying, separating and distinguishing several overlapping, occluding objects in medical images. Applying the proposed theory to the application of cervical cell segmentation from overlapping cell clusters and aggregates, it can be seen that it is possible to formally describe the complex image contents. CONCLUSIONS: The proposed method of semantical segmentation is a mighty tool and under the assumption of the subtractive transparency model can be used in different medical image processing applications such as radiology and microscopy. By using alternative models to solve the ambiguities attached to overlaps and occlusions, further fields of application can be addressed.
Authors: Jenna L Mueller; Zachary T Harmany; Jeffrey K Mito; Stephanie A Kennedy; Yongbaek Kim; Leslie Dodd; Joseph Geradts; David G Kirsch; Rebecca M Willett; J Quincy Brown; Nimmi Ramanujam Journal: PLoS One Date: 2013-06-18 Impact factor: 3.240
Authors: Stephan Wienert; Daniel Heim; Kai Saeger; Albrecht Stenzinger; Michael Beil; Peter Hufnagl; Manfred Dietel; Carsten Denkert; Frederick Klauschen Journal: Sci Rep Date: 2012-07-11 Impact factor: 4.379