Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?

  • Akira YamadaEmail author
  • Kazuki Oyama
  • Sachie Fujita
  • Eriko Yoshizawa
  • Fumihito Ichinohe
  • Daisuke Komatsu
  • Yasunari Fujinaga
Short communication



To evaluate the effect of image registration on the diagnostic performance of transfer learning (TL) using pretrained convolutional neural networks (CNNs) and three-phasic dynamic contrast-enhanced computed tomography (DCE-CT) for primary liver cancers.


We retrospectively evaluated 215 consecutive patients with histologically proven primary liver cancers, including six early, 58 well-differentiated, 109 moderately differentiated, 29 poorly differentiated hepatocellular carcinomas (HCCs), and 13 non-HCC malignant lesions containing cholangiocellular components. We performed TL using various pretrained CNNs and preoperative three-phasic DCE-CT images. Three-phasic DCE-CT images were manually registered to correct respiratory motion. The registered DCE-CT images were then assigned to the three color channels of an input image for TL: pre-contrast, early phase, and delayed phase images for the blue, red, and green channels, respectively. To evaluate the effects of image registration, the registered input image was intentionally misaligned in the three color channels by pixel shifts, rotations, and skews with various degrees. The diagnostic performances (DP) of the pretrained CNNs after TL in the test set were compared by three general radiologists (GRs) and two experienced abdominal radiologists (ARs). The effects of misalignment in the input image and the type of pretrained CNN on the DP were statistically evaluated.


The mean DPs for histological subtype classification and differentiation in primary malignant liver tumors on DCE-CT for GR and AR were 39.1%, and 47.9%, respectively. The highest mean DPs for CNNs after TL with pixel shifts, rotations, and skew misalignments were 44.1%, 44.2%, and 43.7%, respectively. Two-way analysis of variance revealed that the DP is significantly affected by the type of pretrained CNN (P = 0.0001), but not by misalignments in input images other than skew deformations.


TL using pretrained CNNs is robust against misregistration of multiphasic images and comparable to experienced ARs in classifying primary liver cancers using three-phasic DCE-CT.


Primary liver cancer Dynamic contrast-enhanced computed tomography Transfer learning Convolutional neural network Registration 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Torzilli G, Minagawa M, Takayama T, Inoue K, Hui AM, Kubota K, Ohtomo K, Makuuchi M (1999) Accurate preoperative evaluation of liver mass lesions without fine-needle biopsy. Hepatology 30:889–893CrossRefGoogle Scholar
  2. 2.
    Okabe H, Yoshizumi T, Yamashita YI, Imai K, Hayashi H, Nakagawa S, Itoh S, Harimoto N, Ikegami T, Uchiyama H, Beppu T, Aishima S, Shirabe K, Baba H, Maehara Y (2018) Histological architectural classification determines recurrence pattern and prognosis after curative hepatectomy in patients with hepatocellular carcinoma. PLoS ONE 13:e0203856. CrossRefGoogle Scholar
  3. 3.
    Yasaka K, Akai H, Kunimatsu A, Kiryu S, Abe O (2018) Deep learning with convolutional neural network in radiology. Jpn J Radiol 36:257–272. CrossRefGoogle Scholar
  4. 4.
    Byra M, Styczynski G, Szmigielski C, Kalinowski P, Michałowski Ł, Paluszkiewicz R, Ziarkiewicz-Wróblewska B, Zieniewicz K, Sobieraj P, Nowicki A (2018) Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 13:1895–1903. CrossRefGoogle Scholar
  5. 5.
    Yu Y, Wang J, Ng CW, Ma Y, Mo S, Fong ELS, Xing J, Song Z, Xie Y, Si K, Wee A, Welsch RE, So PTC, Yu H (2018) Deep learning enables automated scoring of liver fibrosis stages. Sci Rep 8:16016. CrossRefGoogle Scholar
  6. 6.
    Deng J, Dong W, Socher R, Li L, Li K, FeiFei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition (CVPR).
  7. 7.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE conference on computer vision and pattern recognition (CVPR).
  8. 8.
    Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE conference on computer vision and pattern recognition (CVPR).

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of RadiologyShinshu University School of MedicineMatsumotoJapan

Personalised recommendations