TY - GEN
T1 - Hyperspectral imaging for colon cancer classification in surgical specimens: towards optical biopsy during image-guided surgery
AU - Manni, Francesca
AU - Fonollà, Roger
AU - van der Sommen, Fons
AU - Zinger, Svitlana
AU - Shan, Caifeng
AU - Kho, Esther
AU - Brouwer de Koning, Susan G.
AU - Ruers, Theo J.M.
AU - de With, Peter H.N.
PY - 2020/8/27
Y1 - 2020/8/27
N2 - The main curative treatment for localized colon cancer is surgical resection. However when tumor residuals are left positive margins are found during the histological examinations and additional treatment is needed to inhibit recurrence. Hyperspectral imaging (HSI) can offer non-invasive surgical guidance with the potential of optimizing the surgical effectiveness. In this paper we investigate the capability of HSI for automated colon cancer detection in six ex-vivo specimens employing a spectral-spatial patch-based classification approach. The results demonstrate the feasibility in assessing the benign and malignant boundaries of the lesion with a sensitivity of 0.88 and specificity of 0.78. The results are compared with the state-of-the-art deep learning based approaches. The method with a new hybrid CNN outperforms the state-of the-art approaches (0.74 vs. 0.82 AUC). This study paves the way for further investigation towards improving surgical outcomes with HSI.
AB - The main curative treatment for localized colon cancer is surgical resection. However when tumor residuals are left positive margins are found during the histological examinations and additional treatment is needed to inhibit recurrence. Hyperspectral imaging (HSI) can offer non-invasive surgical guidance with the potential of optimizing the surgical effectiveness. In this paper we investigate the capability of HSI for automated colon cancer detection in six ex-vivo specimens employing a spectral-spatial patch-based classification approach. The results demonstrate the feasibility in assessing the benign and malignant boundaries of the lesion with a sensitivity of 0.88 and specificity of 0.78. The results are compared with the state-of-the-art deep learning based approaches. The method with a new hybrid CNN outperforms the state-of the-art approaches (0.74 vs. 0.82 AUC). This study paves the way for further investigation towards improving surgical outcomes with HSI.
KW - hyperspectral imaging
KW - colon cancer
KW - image-guided surgery
KW - 3D-2D CNN
KW - Neoplasm Recurrence, Local/diagnostic imaging
KW - Biopsy
KW - Colonic Neoplasms/diagnostic imaging
KW - Humans
KW - Surgery, Computer-Assisted
UR - http://www.scopus.com/inward/record.url?scp=85091009762&partnerID=8YFLogxK
U2 - 10.1109/EMBC44109.2020.9176543
DO - 10.1109/EMBC44109.2020.9176543
M3 - Conference contribution
C2 - 33018195
SP - 1169
EP - 1173
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - IEEE EMBS
ER -
