DOI: 10.5061/dryad.qz612jmh1
title: Tsetse fly wing landmark data for morphometrics (Vol 20, 21)

description: Single-wing images were captured from 14,354 pairs of field-collected
 tsetse wings of species Glossina pallidipes and G. m. morsitans and
 analysed together with relevant biological data. To answer research
 questions regarding these flies, we need to locate 11 anatomical landmark
 coordinates on each wing. The manual location of landmarks is
 time-consuming, prone to error, and simply infeasible given the number of
 images. Automatic landmark detection has been proposed to locate these
 landmark coordinates. We developed a two-tier method using deep learning
 architectures to classify images and make accurate landmark predictions.
 The first tier used a classification convolutional neural network to
 remove most wings that were missing landmarks. The second tier provided
 landmark coordinates for the remaining wings. For the second tier,
 compared direct coordinate regression using a convolutional neural network
 and segmentation using a fully convolutional network. For the resulting
 landmark predictions, we evaluate shape bias using Procrustes analysis. We
 employ a data-centric approach paying particular attention to consistent
 labelling and data augmentations in training data to improve model
 performance. The classification model used for the first tier achieved
 perfect classification on the test set. For an image size of 1024×1280,
 data augmentation reduced the mean pixel distance error from 8.3 (95% CI
 [4.4,10.3]) to 5.34 (95% CI [3,7]) for the regression model. For the
 segmentation model, data augmentation did not alter the mean pixel
 distance error of 3.43 (95% CI [1.9,4.4]). Segmentation had a higher
 computational complexity and some large outliers. Both models showed
 minimal shape bias. We chose to deploy the regression model on complete
 unannotated data since the regression model had a lower computational cost
 and more stable predictions than the segmentation model. The resulting
 landmark dataset was provided for future morphometric analysis.
description: This data was collected via field traps designed to catch tsetse
 flies. The fly wings were processed from the flies and laminated on an A4
 sheet of paper along with various biological recordings from a lab
 dissection of the fly. This data was subsequently digitised by recording
 the data for each fly in excel spreadsheets. A microscope camera was used
 to capture a digital image of the fly wings at a resolution of 1024×1280.
 A subset of images was annotated and used to train machine learning
 models. The wing images were then given as inputs to machine learning
 models which located and recorded various landmarks in each fly wing
 image. These landmarks were appended to the dataset of biological
 recording taken during the lab dissection. This data was processed to
 remove outliers and other erroneous instances in the data set.
 The different files in the dataset are described
 below. 
 <strong>tetse_data.csv</strong>
 Column names and description vpn:
 vpn is the filename, identified by the volume (v), page (p), and number
 (n) of the fly. The numbers go up to 20 (20 pairs of wings per a
 page) cd: Day of month captured
 cm: Month of year captured cy: Calender
 year md: Capture method g:
 Genus s: Sex; 1 = male; 2 = female
 c: Ovarian age category i.e. the number of times a female has
 ovulated, varying from 0 to 7. wlm: Wing length in
 millimeters (measured from landmark 1 to 6) f: Wing
 fray, varying from 1-6 lmkr: Number of missing
 landmarks for the right wing (Not accurate) lmkl:
 Number of missing landmarks for the left wing (Not accurate)
 hc: Hatchet cell measurement in millimeters (measured from
 landmark 11 to 7. This was sometimes measured instead of the wing length
 if landmark 1 or 6 was missing) left_good: Classifier
 prediction for the left wing; 0 = incomplete wing; 1 = complete
 wing right_good: Classifier prediction for the right
 wing; 0 = incomplete wing; 1 = complete wing
 l&lt;x/y&gt;&lt;#&gt;: The rest of the columns
 indicate the pixel location of landmark # , with x and y
 coordinate The zipped files <strong>tsetsedata_2019_right_commas.zip</strong> and <strong>tsetsedata_2019_left_commas.zip</strong> contain all the images and landmark coordinate labels that were used the train the landmark detection models referred to in the linked article. The files <strong>model_unet__segmen_finetune_.pth</strong> and <strong>model_resnet50_regressor_finetuned.pth</strong> are trained models used for landmark detection.  The <strong>missinglandmark_wings_L.zip </strong>contains all the broken wings used for training the classifier model.