Liver segmentation and metastases detection in MR images using convolutional neural networks
This work addresses early detection of liver metastases for improved patient outcomes in oncology, representing an incremental advance in medical imaging.
The paper tackled liver metastases detection in MR images by first segmenting the liver using a CNN-based method and then detecting metastases within the mask, achieving a median Dice coefficient of 0.95 for segmentation and 99.8% sensitivity with a median of 2 false positives per image for detection.
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome. We propose a method based on convolutional neural networks (CNN) to detect liver metastases. First, the liver was automatically segmented using the six phases of abdominal dynamic contrast enhanced (DCE) MR images. Next, DCE-MR and diffusion weighted (DW) MR images are used for metastases detection within the liver mask. The liver segmentations have a median Dice similarity coefficient of 0.95 compared with manual annotations. The metastases detection method has a sensitivity of 99.8% with a median of 2 false positives per image. The combination of the two MR sequences in a dual pathway network is proven valuable for the detection of liver metastases. In conclusion, a high quality liver segmentation can be obtained in which we can successfully detect liver metastases.