B-SegNet: Branched-SegMentor network for skin lesion segmentation

Shreshth Saini, YoungSeok Jeon, Mengling Feng

Abstract:

Melanoma is the most common form of cancer in the world. Early
diagnosis of the disease and an accurate estimation of its size and
shape are crucial in preventing its spread to other body parts. Manual segmentation of these lesions by a radiologist however is time
consuming and error-prone. It is clinically desirable to have an
automatic tool to detect malignant skin lesions from dermoscopic
skin images. We propose a novel end-to-end convolution neural
network(CNN) for a precise and robust skin lesion localization and
segmentation. The proposed network has 3 sub-encoders branching out from the main encoder. The 3 sub-encoders are inspired
from Coordinate Convolution, Hourglass and Octave Convolutional
blocks: each sub-encoder summarizes different patterns and yet
collectively aims to achieve a precise segmentation. We trained our
segmentation model just on the ISIC 2018 dataset. To demonstrate
the generalizability of our model, we evaluated our model on the
ISIC 2018 and unseen datasets including ISIC 2017 and PH2
. Our
approach showed an average 5% improvement in performance over
different datasets, while having less than half of the number of
parameters when compared to other state-of-the-arts segmentation
models.