A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
This work addresses the need for efficient medical image analysis in oncology, but it appears incremental as it combines existing machine learning techniques without introducing a fundamentally new approach.
The authors tackled the problem of automated detection and segmentation of liver lesions and pathological lymph nodes in CT images, achieving a detection sensitivity of 0.53 and an average Dice score of 0.71 ± 0.15 for segmentation.
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes. The process is performed in three steps, including organ detection, lesion detection and lesion segmentation. Our method applies machine learning techniques such as marginal space learning and convolutional neural networks, as well as active contour models. The method proves to be robust in its handling of extremely high lesion diversity. We tested our method on volumetric computed tomography (CT) images, including 42 volumes containing liver lesions and 86 volumes containing 595 pathological lymph nodes. Preliminary results under 10-fold cross validation show that for both the liver lesions and the lymph nodes, a total detection sensitivity of 0.53 and average Dice score of $0.71 \pm 0.15$ for segmentation were obtained.