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Organ detection in thorax abdomen CT using multi-label convolutional neural networks

: Humpire Mamani, G.E.; Setio, A.A.A.; Ginneken, B. van; Jacobs, C.


Armato, S.G. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical Imaging 2017. Computer-Aided Diagnosis : 13-16 February 2017, Orlando, Florida, United States
Bellingham, WA: SPIE, 2017 (Proceedings of SPIE 10134)
ISBN: 978-1-5106-0713-2
ISBN: 978-1-5106-0714-9
Paper 1013416, 6 pp.
Conference "Medical Imaging - Computer-Aided Diagnosis" <2017, Orlando/Fla.>
Conference Paper
Fraunhofer MEVIS ()

A convolutional network architecture is presented to determine bounding boxes around six organs in thoraxabdomen CT scans. A single network for each orthogonal view determines the presence of lungs, kidneys, spleen and liver. We show that an architecture that takes additional slices before and after the slice of interest as an additional input outperforms an architecture that processes single slices. From the slice-based analysis, a bounding box around the structures of interest can be computed. The system uses 6 convolutional, 4 pooling and one fully connected layer and uses 333 scans for training and 110 for validation. The test set contains 110 scans. The average Dice score of the proposed method was 0.95 and 0.95 for the lungs, 0.59 and 0.58 for the kidneys, 0.83 for the liver and 0.63 for the spleen. This paper shows that automatic localization of organs using multi-label convolution neural networks is possible. This architecture can likely be used to identify other organs of interest as well.