Semantic Segmentation of Sheep Organs by Convolutional Neural Networks The recognition of individual organs by machine vision is an important step towards the processing of lamb meat and the extraction of edible offal. This paper presents the first semantic segmentation method to localise and label sheep organs in images of the extracted organ package. The challenges around the semantic segmentation of lamb organs include the lack of available data sets, the variable orientation, shape and appearance of organs, and the wet and specular surface. To address these challenges, we created a new dataset for sheep organ segmentation consisting of images and pixelwise labels for seven types of organs and tissues. To produce baseline results on the data set, a convolutional neural network is trained to partition images into segments corresponding to individual organs.

A TwoTier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two stateoftheart CNNs, namely 2D YOLOv2 and 3D UNet, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.