Name
Abstract | ||
|---|---|---|
Cell nuclei detection is a challenging research topic because of limitations in cellular image quality and diversity of nuclear morphology, i.e. varying nuclei shapes, sizes, and overlaps between multiple cell nuclei. This has been a topic of enduring interest with promising recent success shown by deep learning methods. These methods train Convolutional Neural Networks (CNNs) with a training set of input images and known, labeled nuclei locations. Many such methods are supplemented by spatial or morphological processing. Using a set of canonical cell nuclei shapes, prepared with the help of a domain expert, we develop a new approach that we call Shape Priors with Convolutional Neural Networks (SP-CNN). We further extend the network to introduce a shape prior (SP) layer and then allowing it to become trainable (i.e. optimizable). We call this network tunable SP-CNN (TSP-CNN). In summary, we present new network structures that can incorporate 'expected behavior' of nucleus shapes via two components: learnable layers that perform the nucleus detection and a fixed processing part that guides the learning with prior information. Analytically, we formulate two new regularization terms that are targeted at: 1) learning the shapes, 2) reducing false positives while simultaneously encouraging detection inside the cell nucleus boundary. Experimental results on two challenging datasets reveal that the proposed SP-CNN and TSP-CNN can outperform state-of-the-art alternatives. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2019.2895318 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Shape,Image edge detection,Computer architecture,Microprocessors,Deep learning,Biomedical imaging,Image segmentation | Nucleus,Pattern recognition,Subject-matter expert,Computer science,Convolutional neural network,Image quality,Regularization (mathematics),Artificial intelligence,Deep learning,Prior probability,False positive paradox | Journal |
Volume | Issue | ISSN |
abs/1901.07061 | 9 | 1558-254X |
Citations | PageRank | References |
5 | 0.38 | 28 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohammad Tofighi | 1 | 65 | 8.74 |
| Tiantong Guo | 2 | 106 | 7.20 |
| Jairam K. P. Vanamala | 3 | 6 | 0.73 |
| Vishal Monga | 4 | 679 | 57.73 |