Abstract:
According to a survey of global health metrics, cancer is one of top five leading
lethal diseases around the world. It has the capability to proliferate into other parts
of body and often recurs again after the treatment or removal from body. This
immensely growing issue is now-a-days a hot topic among pathologists and
researchers community. Among the analytic factors to study tumor aggressiveness
and disease recurrence, density of micro-vessels (MVD), Lymphovascular invasion
(LVI) and Perineural Invasion (PNI) are considered key prognostic factors. The
manual identification of micro-vessels and nerves is time consuming, laborious and
highly prone to human error. Computational pathology is an emerging field striving
to improve patient care by incorporating modern algorithms to the traditional
analysis procedures of microscopic slides. To overcome the challenges of multi scale, multi-shape and slight intensity variant histopathology structures, a deep
neural network based hybrid semantic segmentation architecture is proposed. It
comprises the fundamentals of encoder-decoder structure with the essence of
parallel path network. The framework is specifically designed to improve the
accuracy by focusing mega to minor object details during every block of
segmentation network. The encoder uses Multi-scale feature extraction block made
up of ResNeXt Blocks. This organization is effective to encode coarse to fine
grained features from all specifications and dimensions while limiting the number
of learnable parameters. The decoder is a combination of feature fusion and feature
erudition while step by step mapping them back to pixel map. Monte-Carlo Dropout
based uncertainty maps are also generated at prediction time. The proposed
architecture is trained and tested on generated Nerve and micro-Vessel Semantic
Segmentation Dataset (NVSSD). The trained architecture outperformed the
existing state-of-the-art networks like FCN, Unet, SegNet, Deeplabv3+
