Intra-operative tumour margin evaluation in breast-conserving surgery
  with deep learning
Wei-Chung Shia Yu-Len Huang Yi-Chun Chen Hwa-Koon Wu Dar-Ren Chen
A positive margin may result in an increased risk of local recurrences after
breast retention surgery for any malignant tumour. In order to reduce the
number of positive margins would offer surgeon real-time intra-operative
information on the presence of positive resection margins. This study aims to
design an intra-operative tumour margin evaluation scheme by using specimen
mammography in breast-conserving surgery. Total of 30 cases were evaluated and
compared with the manually determined contours by experienced physicians and
pathology report. The proposed method utilizes image thresholding to extract
regions of interest and then performs a deep learning model, i.e. SegNet, to
segment tumour tissue. The margin width of normal tissues surrounding it is
evaluated as the result. The desired size of margin around the tumor was set
for 10 mm. The smallest average difference to manual sketched margin (6.53 mm
+- 5.84). In the all case, the SegNet architecture was utilized to obtain
tissue specimen boundary and tumor contour, respectively. The simulation
results indicated that this technology is helpful in discriminating positive
from negative margins in the intra-operative setting. The aim of proposed
scheme was a potential procedure in the intra-operative measurement system. The
experimental results reveal that deep learning techniques can draw results that
are consistent with pathology reports.